---
_id: '45884'
author:
- first_name: Jonas Manuel
full_name: Hanselle, Jonas Manuel
id: '43980'
last_name: Hanselle
orcid: 0000-0002-1231-4985
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
- first_name: Mohamed
full_name: Sherif, Mohamed
id: '67234'
last_name: Sherif
orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
citation:
ama: 'Hanselle JM, Hüllermeier E, Mohr F, et al. Configuration and Evaluation. In:
Haake C-J, Meyer auf der Heide F, Platzner M, Wachsmuth H, Wehrheim H, eds. On-The-Fly
Computing -- Individualized IT-Services in Dynamic Markets. Vol 412. Verlagsschriftenreihe
des Heinz Nixdorf Instituts. Heinz Nixdorf Institut, Universität Paderborn; 2023:85-104.
doi:10.5281/zenodo.8068466'
apa: Hanselle, J. M., Hüllermeier, E., Mohr, F., Ngonga Ngomo, A.-C., Sherif, M.,
Tornede, A., & Wever, M. D. (2023). Configuration and Evaluation. In C.-J.
Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, & H. Wehrheim (Eds.),
On-The-Fly Computing -- Individualized IT-services in dynamic markets (Vol.
412, pp. 85–104). Heinz Nixdorf Institut, Universität Paderborn. https://doi.org/10.5281/zenodo.8068466
bibtex: '@inbook{Hanselle_Hüllermeier_Mohr_Ngonga Ngomo_Sherif_Tornede_Wever_2023,
place={Paderborn}, series={Verlagsschriftenreihe des Heinz Nixdorf Instituts},
title={Configuration and Evaluation}, volume={412}, DOI={10.5281/zenodo.8068466},
booktitle={On-The-Fly Computing -- Individualized IT-services in dynamic markets},
publisher={Heinz Nixdorf Institut, Universität Paderborn}, author={Hanselle, Jonas
Manuel and Hüllermeier, Eyke and Mohr, Felix and Ngonga Ngomo, Axel-Cyrille and
Sherif, Mohamed and Tornede, Alexander and Wever, Marcel Dominik}, editor={Haake,
Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth,
Henning and Wehrheim, Heike}, year={2023}, pages={85–104}, collection={Verlagsschriftenreihe
des Heinz Nixdorf Instituts} }'
chicago: 'Hanselle, Jonas Manuel, Eyke Hüllermeier, Felix Mohr, Axel-Cyrille Ngonga
Ngomo, Mohamed Sherif, Alexander Tornede, and Marcel Dominik Wever. “Configuration
and Evaluation.” In On-The-Fly Computing -- Individualized IT-Services in Dynamic
Markets, edited by Claus-Jochen Haake, Friedhelm Meyer auf der Heide, Marco
Platzner, Henning Wachsmuth, and Heike Wehrheim, 412:85–104. Verlagsschriftenreihe
Des Heinz Nixdorf Instituts. Paderborn: Heinz Nixdorf Institut, Universität Paderborn,
2023. https://doi.org/10.5281/zenodo.8068466.'
ieee: 'J. M. Hanselle et al., “Configuration and Evaluation,” in On-The-Fly
Computing -- Individualized IT-services in dynamic markets, vol. 412, C.-J.
Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim, Eds.
Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 85–104.'
mla: Hanselle, Jonas Manuel, et al. “Configuration and Evaluation.” On-The-Fly
Computing -- Individualized IT-Services in Dynamic Markets, edited by Claus-Jochen
Haake et al., vol. 412, Heinz Nixdorf Institut, Universität Paderborn, 2023, pp.
85–104, doi:10.5281/zenodo.8068466.
short: 'J.M. Hanselle, E. Hüllermeier, F. Mohr, A.-C. Ngonga Ngomo, M. Sherif, A.
Tornede, M.D. Wever, in: C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H.
Wachsmuth, H. Wehrheim (Eds.), On-The-Fly Computing -- Individualized IT-Services
in Dynamic Markets, Heinz Nixdorf Institut, Universität Paderborn, Paderborn,
2023, pp. 85–104.'
date_created: 2023-07-07T07:50:53Z
date_updated: 2023-07-07T11:20:12Z
ddc:
- '040'
department:
- _id: '7'
doi: 10.5281/zenodo.8068466
editor:
- first_name: Claus-Jochen
full_name: Haake, Claus-Jochen
last_name: Haake
- first_name: Friedhelm
full_name: Meyer auf der Heide, Friedhelm
last_name: Meyer auf der Heide
- first_name: Marco
full_name: Platzner, Marco
last_name: Platzner
- first_name: Henning
full_name: Wachsmuth, Henning
last_name: Wachsmuth
- first_name: Heike
full_name: Wehrheim, Heike
last_name: Wehrheim
file:
- access_level: open_access
content_type: application/pdf
creator: florida
date_created: 2023-07-07T07:50:34Z
date_updated: 2023-07-07T11:20:11Z
file_id: '45885'
file_name: B2-Chapter-SFB-Buch-Final.pdf
file_size: 895091
relation: main_file
file_date_updated: 2023-07-07T11:20:11Z
has_accepted_license: '1'
intvolume: ' 412'
language:
- iso: eng
oa: '1'
page: 85-104
place: Paderborn
project:
- _id: '1'
grant_number: '160364472'
name: 'SFB 901: SFB 901: On-The-Fly Computing - Individualisierte IT-Dienstleistungen
in dynamischen Märkten '
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '10'
grant_number: '160364472'
name: 'SFB 901 - B2: Konfiguration und Bewertung (B02)'
publication: On-The-Fly Computing -- Individualized IT-services in dynamic markets
publisher: Heinz Nixdorf Institut, Universität Paderborn
series_title: Verlagsschriftenreihe des Heinz Nixdorf Instituts
status: public
title: Configuration and Evaluation
type: book_chapter
user_id: '477'
volume: 412
year: '2023'
...
---
_id: '45863'
abstract:
- lang: eng
text: "In the proposal for our CRC in 2011, we formulated a vision of markets for\r\nIT
services that describes an approach to the provision of such services\r\nthat
was novel at that time and, to a large extent, remains so today:\r\n„Our vision
of on-the-fly computing is that of IT services individually and\r\nautomatically
configured and brought to execution from flexibly combinable\r\nservices traded
on markets. At the same time, we aim at organizing\r\nmarkets whose participants
maintain a lively market of services through\r\nappropriate entrepreneurial actions.“\r\nOver
the last 12 years, we have developed methods and techniques to\r\naddress problems
critical to the convenient, efficient, and secure use of\r\non-the-fly computing.
Among other things, we have made the description\r\nof services more convenient
by allowing natural language input,\r\nincreased the quality of configured services
through (natural language)\r\ninteraction and more efficient configuration processes
and analysis\r\nprocedures, made the quality of (the products of) providers in
the\r\nmarketplace transparent through reputation systems, and increased the\r\nresource
efficiency of execution through reconfigurable heterogeneous\r\ncomputing nodes
and an integrated treatment of service description and\r\nconfiguration. We have
also developed network infrastructures that have\r\na high degree of adaptivity,
scalability, efficiency, and reliability, and\r\nprovide cryptographic guarantees
of anonymity and security for market\r\nparticipants and their products and services.\r\nTo
demonstrate the pervasiveness of the OTF computing approach, we\r\nhave implemented
a proof-of-concept for OTF computing that can run\r\ntypical scenarios of an OTF
market. We illustrated the approach using\r\na cutting-edge application scenario
– automated machine learning (AutoML).\r\nFinally, we have been pushing our work
for the perpetuation of\r\nOn-The-Fly Computing beyond the SFB and sharing the
expertise gained\r\nin the SFB in events with industry partners as well as transfer
projects.\r\nThis work required a broad spectrum of expertise. Computer scientists\r\nand
economists with research interests such as computer networks and\r\ndistributed
algorithms, security and cryptography, software engineering\r\nand verification,
configuration and machine learning, computer engineering\r\nand HPC, microeconomics
and game theory, business informatics\r\nand management have successfully collaborated
here."
alternative_title:
- Collaborative Research Centre 901 (2011 – 2023)
author:
- first_name: Claus-Jochen
full_name: Haake, Claus-Jochen
id: '20801'
last_name: Haake
- first_name: Friedhelm
full_name: Meyer auf der Heide, Friedhelm
id: '15523'
last_name: Meyer auf der Heide
- first_name: Marco
full_name: Platzner, Marco
id: '398'
last_name: Platzner
- first_name: Henning
full_name: Wachsmuth, Henning
id: '3900'
last_name: Wachsmuth
- first_name: Heike
full_name: Wehrheim, Heike
id: '573'
last_name: Wehrheim
citation:
ama: Haake C-J, Meyer auf der Heide F, Platzner M, Wachsmuth H, Wehrheim H. On-The-Fly
Computing -- Individualized IT-Services in Dynamic Markets. Vol 412. Heinz
Nixdorf Institut, Universität Paderborn; 2023. doi:10.17619/UNIPB/1-1797
apa: Haake, C.-J., Meyer auf der Heide, F., Platzner, M., Wachsmuth, H., & Wehrheim,
H. (2023). On-The-Fly Computing -- Individualized IT-services in dynamic markets
(Vol. 412). Heinz Nixdorf Institut, Universität Paderborn. https://doi.org/10.17619/UNIPB/1-1797
bibtex: '@book{Haake_Meyer auf der Heide_Platzner_Wachsmuth_Wehrheim_2023, place={Paderborn},
series={Verlagsschriftenreihe des Heinz Nixdorf Instituts}, title={On-The-Fly
Computing -- Individualized IT-services in dynamic markets}, volume={412}, DOI={10.17619/UNIPB/1-1797}, publisher={Heinz
Nixdorf Institut, Universität Paderborn}, author={Haake, Claus-Jochen and Meyer
auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth, Henning and Wehrheim,
Heike}, year={2023}, collection={Verlagsschriftenreihe des Heinz Nixdorf Instituts}
}'
chicago: 'Haake, Claus-Jochen, Friedhelm Meyer auf der Heide, Marco Platzner, Henning
Wachsmuth, and Heike Wehrheim. On-The-Fly Computing -- Individualized IT-Services
in Dynamic Markets. Vol. 412. Verlagsschriftenreihe Des Heinz Nixdorf Instituts.
Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023. https://doi.org/10.17619/UNIPB/1-1797.'
ieee: 'C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim,
On-The-Fly Computing -- Individualized IT-services in dynamic markets,
vol. 412. Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023.'
mla: Haake, Claus-Jochen, et al. On-The-Fly Computing -- Individualized IT-Services
in Dynamic Markets. Heinz Nixdorf Institut, Universität Paderborn, 2023, doi:10.17619/UNIPB/1-1797.
short: C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, H. Wehrheim,
On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets, Heinz Nixdorf
Institut, Universität Paderborn, Paderborn, 2023.
date_created: 2023-07-05T07:16:51Z
date_updated: 2023-08-29T06:44:36Z
ddc:
- '000'
department:
- _id: '7'
doi: 10.17619/UNIPB/1-1797
file:
- access_level: open_access
content_type: application/pdf
creator: ups
date_created: 2023-07-05T07:15:55Z
date_updated: 2023-07-05T07:19:14Z
file_id: '45864'
file_name: SFB-Buch-Final.pdf
file_size: 15480050
relation: main_file
file_date_updated: 2023-07-05T07:19:14Z
has_accepted_license: '1'
intvolume: ' 412'
language:
- iso: eng
oa: '1'
page: '247'
place: Paderborn
project:
- _id: '1'
grant_number: '160364472'
name: 'SFB 901: SFB 901: On-The-Fly Computing - Individualisierte IT-Dienstleistungen
in dynamischen Märkten '
- _id: '2'
name: 'SFB 901 - A: SFB 901 - Project Area A'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '4'
name: 'SFB 901 - C: SFB 901 - Project Area C'
- _id: '82'
name: 'SFB 901 - T: SFB 901 - Project Area T'
- _id: '5'
grant_number: '160364472'
name: 'SFB 901 - A1: SFB 901 - Möglichkeiten und Grenzen lokaler Strategien in dynamischen
Netzen (Subproject A1)'
- _id: '7'
grant_number: '160364472'
name: 'SFB 901 - A3: SFB 901 - Der Markt für Services: Anreize, Algorithmen, Implementation
(Subproject A3)'
- _id: '8'
grant_number: '160364472'
name: 'SFB 901 - A4: SFB 901 - Empirische Analysen in Märkten für OTF Dienstleistungen
(Subproject A4)'
- _id: '9'
grant_number: '160364472'
name: 'SFB 901 - B1: SFB 901 - Parametrisierte Servicespezifikation (Subproject
B1)'
- _id: '10'
grant_number: '160364472'
name: 'SFB 901 - B2: Konfiguration und Bewertung (B02)'
- _id: '11'
name: 'SFB 901 - B3: SFB 901 - Subproject B3'
- _id: '12'
name: 'SFB 901 - B4: SFB 901 - Subproject B4'
- _id: '13'
name: 'SFB 901 - C1: SFB 901 - Subproject C1'
- _id: '14'
grant_number: '160364472'
name: 'SFB 901 - C2: SFB 901 - On-The-Fly Compute Centers I: Heterogene Ausführungsumgebungen
(Subproject C2)'
- _id: '16'
grant_number: '160364472'
name: 'SFB 901 - C4: SFB 901 - On-The-Fly Compute Centers II: Ausführung komponierter
Dienste in konfigurierbaren Rechenzentren (Subproject C4)'
- _id: '17'
name: 'SFB 901 - C5: SFB 901 - Subproject C5'
- _id: '83'
name: 'SFB 901 - T1: SFB 901 -Subproject T1'
- _id: '84'
name: 'SFB 901 - T2: SFB 901 -Subproject T2'
publication_identifier:
unknown:
- 978-3-947647-31-6
publisher: Heinz Nixdorf Institut, Universität Paderborn
series_title: Verlagsschriftenreihe des Heinz Nixdorf Instituts
status: public
title: On-The-Fly Computing -- Individualized IT-services in dynamic markets
type: book
user_id: '477'
volume: 412
year: '2023'
...
---
_id: '30868'
abstract:
- lang: eng
text: "Algorithm configuration (AC) is concerned with the automated search of the\r\nmost
suitable parameter configuration of a parametrized algorithm. There is\r\ncurrently
a wide variety of AC problem variants and methods proposed in the\r\nliterature.
Existing reviews do not take into account all derivatives of the AC\r\nproblem,
nor do they offer a complete classification scheme. To this end, we\r\nintroduce
taxonomies to describe the AC problem and features of configuration\r\nmethods,
respectively. We review existing AC literature within the lens of our\r\ntaxonomies,
outline relevant design choices of configuration approaches,\r\ncontrast methods
and problem variants against each other, and describe the\r\nstate of AC in industry.
Finally, our review provides researchers and\r\npractitioners with a look at future
research directions in the field of AC."
author:
- first_name: Elias
full_name: Schede, Elias
last_name: Schede
- first_name: Jasmin
full_name: Brandt, Jasmin
last_name: Brandt
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Viktor
full_name: Bengs, Viktor
id: '76599'
last_name: Bengs
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Kevin
full_name: Tierney, Kevin
last_name: Tierney
citation:
ama: Schede E, Brandt J, Tornede A, et al. A Survey of Methods for Automated Algorithm
Configuration. arXiv:220201651. Published online 2022.
apa: Schede, E., Brandt, J., Tornede, A., Wever, M. D., Bengs, V., Hüllermeier,
E., & Tierney, K. (2022). A Survey of Methods for Automated Algorithm Configuration.
In arXiv:2202.01651.
bibtex: '@article{Schede_Brandt_Tornede_Wever_Bengs_Hüllermeier_Tierney_2022, title={A
Survey of Methods for Automated Algorithm Configuration}, journal={arXiv:2202.01651},
author={Schede, Elias and Brandt, Jasmin and Tornede, Alexander and Wever, Marcel
Dominik and Bengs, Viktor and Hüllermeier, Eyke and Tierney, Kevin}, year={2022}
}'
chicago: Schede, Elias, Jasmin Brandt, Alexander Tornede, Marcel Dominik Wever,
Viktor Bengs, Eyke Hüllermeier, and Kevin Tierney. “A Survey of Methods for Automated
Algorithm Configuration.” ArXiv:2202.01651, 2022.
ieee: E. Schede et al., “A Survey of Methods for Automated Algorithm Configuration,”
arXiv:2202.01651. 2022.
mla: Schede, Elias, et al. “A Survey of Methods for Automated Algorithm Configuration.”
ArXiv:2202.01651, 2022.
short: E. Schede, J. Brandt, A. Tornede, M.D. Wever, V. Bengs, E. Hüllermeier, K.
Tierney, ArXiv:2202.01651 (2022).
date_created: 2022-04-12T12:00:08Z
date_updated: 2022-04-12T12:01:15Z
department:
- _id: '34'
- _id: '7'
- _id: '26'
external_id:
arxiv:
- '2202.01651'
language:
- iso: eng
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '10'
name: 'SFB 901 - B2: SFB 901 - Subproject B2'
publication: arXiv:2202.01651
status: public
title: A Survey of Methods for Automated Algorithm Configuration
type: preprint
user_id: '38209'
year: '2022'
...
---
_id: '34103'
abstract:
- lang: eng
text: "It is well known that different algorithms perform differently well on an\r\ninstance
of an algorithmic problem, motivating algorithm selection (AS): Given\r\nan instance
of an algorithmic problem, which is the most suitable algorithm to\r\nsolve it?
As such, the AS problem has received considerable attention resulting\r\nin various
approaches - many of which either solve a regression or ranking\r\nproblem under
the hood. Although both of these formulations yield very natural\r\nways to tackle
AS, they have considerable weaknesses. On the one hand,\r\ncorrectly predicting
the performance of an algorithm on an instance is a\r\nsufficient, but not a necessary
condition to produce a correct ranking over\r\nalgorithms and in particular ranking
the best algorithm first. On the other\r\nhand, classical ranking approaches often
do not account for concrete\r\nperformance values available in the training data,
but only leverage rankings\r\ncomposed from such data. We propose HARRIS- Hybrid
rAnking and RegRessIon\r\nforeSts - a new algorithm selector leveraging special
forests, combining the\r\nstrengths of both approaches while alleviating their
weaknesses. HARRIS'\r\ndecisions are based on a forest model, whose trees are
created based on splits\r\noptimized on a hybrid ranking and regression loss function.
As our preliminary\r\nexperimental study on ASLib shows, HARRIS improves over
standard algorithm\r\nselection approaches on some scenarios showing that combining
ranking and\r\nregression in trees is indeed promising for AS."
author:
- first_name: Lukass
full_name: Fehring, Lukass
last_name: Fehring
- first_name: Jonas Manuel
full_name: Hanselle, Jonas Manuel
id: '43980'
last_name: Hanselle
orcid: 0000-0002-1231-4985
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
citation:
ama: 'Fehring L, Hanselle JM, Tornede A. HARRIS: Hybrid Ranking and Regression Forests
for Algorithm Selection. In: Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS
2022. ; 2022.'
apa: 'Fehring, L., Hanselle, J. M., & Tornede, A. (2022). HARRIS: Hybrid Ranking
and Regression Forests for Algorithm Selection. Workshop on Meta-Learning (MetaLearn
2022) @ NeurIPS 2022. Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS
2022, Baltimore.'
bibtex: '@inproceedings{Fehring_Hanselle_Tornede_2022, title={HARRIS: Hybrid Ranking
and Regression Forests for Algorithm Selection}, booktitle={Workshop on Meta-Learning
(MetaLearn 2022) @ NeurIPS 2022}, author={Fehring, Lukass and Hanselle, Jonas
Manuel and Tornede, Alexander}, year={2022} }'
chicago: 'Fehring, Lukass, Jonas Manuel Hanselle, and Alexander Tornede. “HARRIS:
Hybrid Ranking and Regression Forests for Algorithm Selection.” In Workshop
on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022, 2022.'
ieee: 'L. Fehring, J. M. Hanselle, and A. Tornede, “HARRIS: Hybrid Ranking and Regression
Forests for Algorithm Selection,” presented at the Workshop on Meta-Learning (MetaLearn
2022) @ NeurIPS 2022, Baltimore, 2022.'
mla: 'Fehring, Lukass, et al. “HARRIS: Hybrid Ranking and Regression Forests for
Algorithm Selection.” Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS
2022, 2022.'
short: 'L. Fehring, J.M. Hanselle, A. Tornede, in: Workshop on Meta-Learning (MetaLearn
2022) @ NeurIPS 2022, 2022.'
conference:
location: Baltimore
name: Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022
date_created: 2022-11-17T12:57:40Z
date_updated: 2022-11-17T13:00:53Z
external_id:
arxiv:
- '2210.17341'
language:
- iso: eng
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '10'
name: 'SFB 901 - B2: SFB 901 - Subproject B2'
publication: Workshop on Meta-Learning (MetaLearn 2022) @ NeurIPS 2022
status: public
title: 'HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection'
type: conference
user_id: '38209'
year: '2022'
...
---
_id: '31806'
abstract:
- lang: eng
text: The creation of an RDF knowledge graph for a particular application commonly
involves a pipeline of tools that transform a set ofinput data sources into an
RDF knowledge graph in a process called dataset augmentation. The components of
such augmentation pipelines often require extensive configuration to lead to satisfactory
results. Thus, non-experts are often unable to use them. Wepresent an efficient
supervised algorithm based on genetic programming for learning knowledge graph
augmentation pipelines of arbitrary length. Our approach uses multi-expression
learning to learn augmentation pipelines able to achieve a high F-measure on the
training data. Our evaluation suggests that our approach can efficiently learn
a larger class of RDF dataset augmentation tasks than the state of the art while
using only a single training example. Even on the most complex augmentation problem
we posed, our approach consistently achieves an average F1-measure of 99% in under
500 iterations with an average runtime of 16 seconds
author:
- first_name: Kevin
full_name: Dreßler, Kevin
id: '78256'
last_name: Dreßler
- first_name: Mohamed
full_name: Sherif, Mohamed
id: '67234'
last_name: Sherif
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
citation:
ama: 'Dreßler K, Sherif M, Ngonga Ngomo A-C. ADAGIO - Automated Data Augmentation
of Knowledge Graphs Using Multi-expression Learning. In: Proceedings of the
33rd ACM Conference on Hypertext and Hypermedia. ; 2022. doi:10.1145/3511095.3531287'
apa: 'Dreßler, K., Sherif, M., & Ngonga Ngomo, A.-C. (2022). ADAGIO - Automated
Data Augmentation of Knowledge Graphs Using Multi-expression Learning. Proceedings
of the 33rd ACM Conference on Hypertext and Hypermedia. HT ’22: 33rd ACM Conference
on Hypertext and Social Media, Barcelona (Spain). https://doi.org/10.1145/3511095.3531287'
bibtex: '@inproceedings{Dreßler_Sherif_Ngonga Ngomo_2022, title={ADAGIO - Automated
Data Augmentation of Knowledge Graphs Using Multi-expression Learning}, DOI={10.1145/3511095.3531287}, booktitle={Proceedings
of the 33rd ACM Conference on Hypertext and Hypermedia}, author={Dreßler, Kevin
and Sherif, Mohamed and Ngonga Ngomo, Axel-Cyrille}, year={2022} }'
chicago: Dreßler, Kevin, Mohamed Sherif, and Axel-Cyrille Ngonga Ngomo. “ADAGIO
- Automated Data Augmentation of Knowledge Graphs Using Multi-Expression Learning.”
In Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia,
2022. https://doi.org/10.1145/3511095.3531287.
ieee: 'K. Dreßler, M. Sherif, and A.-C. Ngonga Ngomo, “ADAGIO - Automated Data Augmentation
of Knowledge Graphs Using Multi-expression Learning,” presented at the HT ’22:
33rd ACM Conference on Hypertext and Social Media, Barcelona (Spain), 2022, doi:
10.1145/3511095.3531287.'
mla: Dreßler, Kevin, et al. “ADAGIO - Automated Data Augmentation of Knowledge Graphs
Using Multi-Expression Learning.” Proceedings of the 33rd ACM Conference on
Hypertext and Hypermedia, 2022, doi:10.1145/3511095.3531287.
short: 'K. Dreßler, M. Sherif, A.-C. Ngonga Ngomo, in: Proceedings of the 33rd ACM
Conference on Hypertext and Hypermedia, 2022.'
conference:
end_date: 2022-07-01
location: Barcelona (Spain)
name: 'HT ’22: 33rd ACM Conference on Hypertext and Social Media'
start_date: 2022-06-28
date_created: 2022-06-08T08:47:33Z
date_updated: 2022-11-18T10:11:38Z
ddc:
- '000'
department:
- _id: '34'
doi: 10.1145/3511095.3531287
keyword:
- 2022 RAKI SFB901 deer dice kevin knowgraphs limes ngonga sherif simba
language:
- iso: eng
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '10'
name: 'SFB 901 - B2: SFB 901 - Subproject B2'
publication: Proceedings of the 33rd ACM Conference on Hypertext and Hypermedia
status: public
title: ADAGIO - Automated Data Augmentation of Knowledge Graphs Using Multi-expression
Learning
type: conference
user_id: '477'
year: '2022'
...
---
_id: '30867'
abstract:
- lang: eng
text: "In online algorithm selection (OAS), instances of an algorithmic problem\r\nclass
are presented to an agent one after another, and the agent has to quickly\r\nselect
a presumably best algorithm from a fixed set of candidate algorithms.\r\nFor decision
problems such as satisfiability (SAT), quality typically refers to\r\nthe algorithm's
runtime. As the latter is known to exhibit a heavy-tail\r\ndistribution, an algorithm
is normally stopped when exceeding a predefined\r\nupper time limit. As a consequence,
machine learning methods used to optimize\r\nan algorithm selection strategy in
a data-driven manner need to deal with\r\nright-censored samples, a problem that
has received little attention in the\r\nliterature so far. In this work, we revisit
multi-armed bandit algorithms for\r\nOAS and discuss their capability of dealing
with the problem. Moreover, we\r\nadapt them towards runtime-oriented losses,
allowing for partially censored\r\ndata while keeping a space- and time-complexity
independent of the time\r\nhorizon. In an extensive experimental evaluation on
an adapted version of the\r\nASlib benchmark, we demonstrate that theoretically
well-founded methods based\r\non Thompson sampling perform specifically strong
and improve in comparison to\r\nexisting methods."
author:
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Viktor
full_name: Bengs, Viktor
id: '76599'
last_name: Bengs
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Tornede A, Bengs V, Hüllermeier E. Machine Learning for Online Algorithm Selection
under Censored Feedback. Proceedings of the 36th AAAI Conference on Artificial
Intelligence. Published online 2022.
apa: Tornede, A., Bengs, V., & Hüllermeier, E. (2022). Machine Learning for
Online Algorithm Selection under Censored Feedback. In Proceedings of the 36th
AAAI Conference on Artificial Intelligence. AAAI.
bibtex: '@article{Tornede_Bengs_Hüllermeier_2022, title={Machine Learning for Online
Algorithm Selection under Censored Feedback}, journal={Proceedings of the 36th
AAAI Conference on Artificial Intelligence}, publisher={AAAI}, author={Tornede,
Alexander and Bengs, Viktor and Hüllermeier, Eyke}, year={2022} }'
chicago: Tornede, Alexander, Viktor Bengs, and Eyke Hüllermeier. “Machine Learning
for Online Algorithm Selection under Censored Feedback.” Proceedings of the
36th AAAI Conference on Artificial Intelligence. AAAI, 2022.
ieee: A. Tornede, V. Bengs, and E. Hüllermeier, “Machine Learning for Online Algorithm
Selection under Censored Feedback,” Proceedings of the 36th AAAI Conference
on Artificial Intelligence. AAAI, 2022.
mla: Tornede, Alexander, et al. “Machine Learning for Online Algorithm Selection
under Censored Feedback.” Proceedings of the 36th AAAI Conference on Artificial
Intelligence, AAAI, 2022.
short: A. Tornede, V. Bengs, E. Hüllermeier, Proceedings of the 36th AAAI Conference
on Artificial Intelligence (2022).
date_created: 2022-04-12T11:58:56Z
date_updated: 2022-08-24T12:44:27Z
department:
- _id: '34'
- _id: '7'
- _id: '26'
external_id:
arxiv:
- '2109.06234'
language:
- iso: eng
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '10'
name: 'SFB 901 - B2: SFB 901 - Subproject B2'
publication: Proceedings of the 36th AAAI Conference on Artificial Intelligence
publisher: AAAI
status: public
title: Machine Learning for Online Algorithm Selection under Censored Feedback
type: preprint
user_id: '38209'
year: '2022'
...
---
_id: '30865'
abstract:
- lang: eng
text: "The problem of selecting an algorithm that appears most suitable for a\r\nspecific
instance of an algorithmic problem class, such as the Boolean\r\nsatisfiability
problem, is called instance-specific algorithm selection. Over\r\nthe past decade,
the problem has received considerable attention, resulting in\r\na number of different
methods for algorithm selection. Although most of these\r\nmethods are based on
machine learning, surprisingly little work has been done\r\non meta learning,
that is, on taking advantage of the complementarity of\r\nexisting algorithm selection
methods in order to combine them into a single\r\nsuperior algorithm selector.
In this paper, we introduce the problem of meta\r\nalgorithm selection, which
essentially asks for the best way to combine a given\r\nset of algorithm selectors.
We present a general methodological framework for\r\nmeta algorithm selection
as well as several concrete learning methods as\r\ninstantiations of this framework,
essentially combining ideas of meta learning\r\nand ensemble learning. In an extensive
experimental evaluation, we demonstrate\r\nthat ensembles of algorithm selectors
can significantly outperform single\r\nalgorithm selectors and have the potential
to form the new state of the art in\r\nalgorithm selection."
author:
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Lukas
full_name: Gehring, Lukas
last_name: Gehring
- first_name: Tanja
full_name: Tornede, Tanja
id: '40795'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Tornede A, Gehring L, Tornede T, Wever MD, Hüllermeier E. Algorithm Selection
on a Meta Level. Machine Learning. Published online 2022.
apa: Tornede, A., Gehring, L., Tornede, T., Wever, M. D., & Hüllermeier, E.
(2022). Algorithm Selection on a Meta Level. In Machine Learning.
bibtex: '@article{Tornede_Gehring_Tornede_Wever_Hüllermeier_2022, title={Algorithm
Selection on a Meta Level}, journal={Machine Learning}, author={Tornede, Alexander
and Gehring, Lukas and Tornede, Tanja and Wever, Marcel Dominik and Hüllermeier,
Eyke}, year={2022} }'
chicago: Tornede, Alexander, Lukas Gehring, Tanja Tornede, Marcel Dominik Wever,
and Eyke Hüllermeier. “Algorithm Selection on a Meta Level.” Machine Learning,
2022.
ieee: A. Tornede, L. Gehring, T. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm
Selection on a Meta Level,” Machine Learning. 2022.
mla: Tornede, Alexander, et al. “Algorithm Selection on a Meta Level.” Machine
Learning, 2022.
short: A. Tornede, L. Gehring, T. Tornede, M.D. Wever, E. Hüllermeier, Machine Learning
(2022).
date_created: 2022-04-12T11:55:18Z
date_updated: 2022-08-24T12:45:39Z
department:
- _id: '34'
- _id: '7'
- _id: '26'
external_id:
arxiv:
- '2107.09414'
language:
- iso: eng
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '10'
name: 'SFB 901 - B2: SFB 901 - Subproject B2'
publication: Machine Learning
status: public
title: Algorithm Selection on a Meta Level
type: preprint
user_id: '38209'
year: '2022'
...
---
_id: '33090'
abstract:
- lang: eng
text: 'AbstractHeated tool butt welding is a method
often used for joining thermoplastics, especially when the components are made
out of different materials. The quality of the connection between the components
crucially depends on a suitable choice of the parameters of the welding process,
such as heating time, temperature, and the precise way how the parts are then
welded. Moreover, when different materials are to be joined, the parameter values
need to be tailored to the specifics of the respective material. To this end,
in this paper, three approaches to tailor the parameter values to optimize the
quality of the connection are compared: a heuristic by Potente, statistical experimental
design, and Bayesian optimization. With the suitability for practice in mind,
a series of experiments are carried out with these approaches, and their capabilities
of proposing well-performing parameter values are investigated. As a result, Bayesian
optimization is found to yield peak performance, but the costs for optimization
are substantial. In contrast, the Potente heuristic does not require any experimentation
and recommends parameter values with competitive quality.'
author:
- first_name: Karina
full_name: Gevers, Karina
id: '83151'
last_name: Gevers
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Volker
full_name: Schöppner, Volker
id: '20530'
last_name: Schöppner
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Gevers K, Tornede A, Wever MD, Schöppner V, Hüllermeier E. A comparison of
heuristic, statistical, and machine learning methods for heated tool butt welding
of two different materials. Welding in the World. Published online 2022.
doi:10.1007/s40194-022-01339-9
apa: Gevers, K., Tornede, A., Wever, M. D., Schöppner, V., & Hüllermeier, E.
(2022). A comparison of heuristic, statistical, and machine learning methods for
heated tool butt welding of two different materials. Welding in the World.
https://doi.org/10.1007/s40194-022-01339-9
bibtex: '@article{Gevers_Tornede_Wever_Schöppner_Hüllermeier_2022, title={A comparison
of heuristic, statistical, and machine learning methods for heated tool butt welding
of two different materials}, DOI={10.1007/s40194-022-01339-9},
journal={Welding in the World}, publisher={Springer Science and Business Media
LLC}, author={Gevers, Karina and Tornede, Alexander and Wever, Marcel Dominik
and Schöppner, Volker and Hüllermeier, Eyke}, year={2022} }'
chicago: Gevers, Karina, Alexander Tornede, Marcel Dominik Wever, Volker Schöppner,
and Eyke Hüllermeier. “A Comparison of Heuristic, Statistical, and Machine Learning
Methods for Heated Tool Butt Welding of Two Different Materials.” Welding in
the World, 2022. https://doi.org/10.1007/s40194-022-01339-9.
ieee: 'K. Gevers, A. Tornede, M. D. Wever, V. Schöppner, and E. Hüllermeier, “A
comparison of heuristic, statistical, and machine learning methods for heated
tool butt welding of two different materials,” Welding in the World, 2022,
doi: 10.1007/s40194-022-01339-9.'
mla: Gevers, Karina, et al. “A Comparison of Heuristic, Statistical, and Machine
Learning Methods for Heated Tool Butt Welding of Two Different Materials.” Welding
in the World, Springer Science and Business Media LLC, 2022, doi:10.1007/s40194-022-01339-9.
short: K. Gevers, A. Tornede, M.D. Wever, V. Schöppner, E. Hüllermeier, Welding
in the World (2022).
date_created: 2022-08-24T12:51:07Z
date_updated: 2022-08-24T12:52:06Z
doi: 10.1007/s40194-022-01339-9
keyword:
- Metals and Alloys
- Mechanical Engineering
- Mechanics of Materials
language:
- iso: eng
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '10'
name: 'SFB 901 - B2: SFB 901 - Subproject B2'
publication: Welding in the World
publication_identifier:
issn:
- 0043-2288
- 1878-6669
publication_status: published
publisher: Springer Science and Business Media LLC
status: public
title: A comparison of heuristic, statistical, and machine learning methods for heated
tool butt welding of two different materials
type: journal_article
user_id: '38209'
year: '2022'
...
---
_id: '28350'
abstract:
- lang: eng
text: "In recent years, we observe an increasing amount of software with machine
learning components being deployed. This poses the question of quality assurance
for such components: how can we validate whether specified requirements are fulfilled
by a machine learned software? Current testing and verification approaches either
focus on a single requirement (e.g., fairness) or specialize on a single type
of machine learning model (e.g., neural networks).\r\nIn this paper, we propose
property-driven testing of machine learning models. Our approach MLCheck encompasses
(1) a language for property specification, and (2) a technique for systematic
test case generation. The specification language is comparable to property-based
testing languages. Test case generation employs advanced verification technology
for a systematic, property dependent construction of test suites, without additional
user supplied generator functions. We evaluate MLCheck using requirements and
data sets from three different application areas (software\r\ndiscrimination,
learning on knowledge graphs and security). Our evaluation shows that despite
its generality MLCheck can even outperform specialised testing approaches while
having a comparable runtime"
author:
- first_name: Arnab
full_name: Sharma, Arnab
id: '67200'
last_name: Sharma
- first_name: Caglar
full_name: Demir, Caglar
id: '43817'
last_name: Demir
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
- first_name: Heike
full_name: Wehrheim, Heike
id: '573'
last_name: Wehrheim
citation:
ama: 'Sharma A, Demir C, Ngonga Ngomo A-C, Wehrheim H. MLCHECK–Property-Driven Testing
of Machine Learning Classifiers. In: Proceedings of the 20th IEEE International
Conference on Machine Learning and Applications (ICMLA). IEEE.'
apa: Sharma, A., Demir, C., Ngonga Ngomo, A.-C., & Wehrheim, H. (n.d.). MLCHECK–Property-Driven
Testing of Machine Learning Classifiers. Proceedings of the 20th IEEE International
Conference on Machine Learning and Applications (ICMLA).
bibtex: '@inproceedings{Sharma_Demir_Ngonga Ngomo_Wehrheim, title={MLCHECK–Property-Driven
Testing of Machine Learning Classifiers}, booktitle={Proceedings of the 20th IEEE
International Conference on Machine Learning and Applications (ICMLA)}, publisher={IEEE},
author={Sharma, Arnab and Demir, Caglar and Ngonga Ngomo, Axel-Cyrille and Wehrheim,
Heike} }'
chicago: Sharma, Arnab, Caglar Demir, Axel-Cyrille Ngonga Ngomo, and Heike Wehrheim.
“MLCHECK–Property-Driven Testing of Machine Learning Classifiers.” In Proceedings
of the 20th IEEE International Conference on Machine Learning and Applications
(ICMLA). IEEE, n.d.
ieee: A. Sharma, C. Demir, A.-C. Ngonga Ngomo, and H. Wehrheim, “MLCHECK–Property-Driven
Testing of Machine Learning Classifiers.”
mla: Sharma, Arnab, et al. “MLCHECK–Property-Driven Testing of Machine Learning
Classifiers.” Proceedings of the 20th IEEE International Conference on Machine
Learning and Applications (ICMLA), IEEE.
short: 'A. Sharma, C. Demir, A.-C. Ngonga Ngomo, H. Wehrheim, in: Proceedings of
the 20th IEEE International Conference on Machine Learning and Applications (ICMLA),
IEEE, n.d.'
date_created: 2021-12-07T11:11:36Z
date_updated: 2022-01-06T06:58:02Z
department:
- _id: '7'
- _id: '77'
- _id: '574'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '11'
name: SFB 901 - Subproject B3
- _id: '10'
name: SFB 901 - Subproject B2
publication: Proceedings of the 20th IEEE International Conference on Machine Learning
and Applications (ICMLA)
publication_status: accepted
publisher: IEEE
status: public
title: MLCHECK–Property-Driven Testing of Machine Learning Classifiers
type: conference
user_id: '477'
year: '2021'
...
---
_id: '21004'
abstract:
- lang: eng
text: 'Automated machine learning (AutoML) supports the algorithmic construction
and data-specific customization of machine learning pipelines, including the selection,
combination, and parametrization of machine learning algorithms as main constituents.
Generally speaking, AutoML approaches comprise two major components: a search
space model and an optimizer for traversing the space. Recent approaches have
shown impressive results in the realm of supervised learning, most notably (single-label)
classification (SLC). Moreover, first attempts at extending these approaches towards
multi-label classification (MLC) have been made. While the space of candidate
pipelines is already huge in SLC, the complexity of the search space is raised
to an even higher power in MLC. One may wonder, therefore, whether and to what
extent optimizers established for SLC can scale to this increased complexity,
and how they compare to each other. This paper makes the following contributions:
First, we survey existing approaches to AutoML for MLC. Second, we augment these
approaches with optimizers not previously tried for MLC. Third, we propose a benchmarking
framework that supports a fair and systematic comparison. Fourth, we conduct an
extensive experimental study, evaluating the methods on a suite of MLC problems.
We find a grammar-based best-first search to compare favorably to other optimizers.'
author:
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Wever MD, Tornede A, Mohr F, Hüllermeier E. AutoML for Multi-Label Classification:
Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and
Machine Intelligence. Published online 2021:1-1. doi:10.1109/tpami.2021.3051276'
apa: 'Wever, M. D., Tornede, A., Mohr, F., & Hüllermeier, E. (2021). AutoML
for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions
on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2021.3051276'
bibtex: '@article{Wever_Tornede_Mohr_Hüllermeier_2021, title={AutoML for Multi-Label
Classification: Overview and Empirical Evaluation}, DOI={10.1109/tpami.2021.3051276},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, author={Wever,
Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke},
year={2021}, pages={1–1} }'
chicago: 'Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier.
“AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” IEEE
Transactions on Pattern Analysis and Machine Intelligence, 2021, 1–1. https://doi.org/10.1109/tpami.2021.3051276.'
ieee: 'M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “AutoML for Multi-Label
Classification: Overview and Empirical Evaluation,” IEEE Transactions on Pattern
Analysis and Machine Intelligence, pp. 1–1, 2021, doi: 10.1109/tpami.2021.3051276.'
mla: 'Wever, Marcel Dominik, et al. “AutoML for Multi-Label Classification: Overview
and Empirical Evaluation.” IEEE Transactions on Pattern Analysis and Machine
Intelligence, 2021, pp. 1–1, doi:10.1109/tpami.2021.3051276.'
short: M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, IEEE Transactions on Pattern
Analysis and Machine Intelligence (2021) 1–1.
date_created: 2021-01-16T14:48:13Z
date_updated: 2022-01-06T06:54:42Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
doi: 10.1109/tpami.2021.3051276
keyword:
- Automated Machine Learning
- Multi Label Classification
- Hierarchical Planning
- Bayesian Optimization
language:
- iso: eng
page: 1-1
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_identifier:
issn:
- 0162-8828
- 2160-9292
- 1939-3539
publication_status: published
status: public
title: 'AutoML for Multi-Label Classification: Overview and Empirical Evaluation'
type: journal_article
user_id: '5786'
year: '2021'
...
---
_id: '21092'
abstract:
- lang: eng
text: "Automated Machine Learning (AutoML) seeks to automatically find so-called
machine learning pipelines that maximize the prediction performance when being
used to train a model on a given dataset. One of the main and yet open challenges
in AutoML is an effective use of computational resources: An AutoML process involves
the evaluation of many candidate pipelines, which are costly but often ineffective
because they are canceled due to a timeout.\r\nIn this paper, we present an approach
to predict the runtime of two-step machine learning pipelines with up to one pre-processor,
which can be used to anticipate whether or not a pipeline will time out. Separate
runtime models are trained offline for each algorithm that may be used in a pipeline,
and an overall prediction is derived from these models. We empirically show that
the approach increases successful evaluations made by an AutoML tool while preserving
or even improving on the previously best solutions."
author:
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Mohr F, Wever MD, Tornede A, Hüllermeier E. Predicting Machine Learning Pipeline
Runtimes in the Context of Automated Machine Learning. IEEE Transactions on
Pattern Analysis and Machine Intelligence.
apa: Mohr, F., Wever, M. D., Tornede, A., & Hüllermeier, E. (n.d.). Predicting
Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning.
IEEE Transactions on Pattern Analysis and Machine Intelligence.
bibtex: '@article{Mohr_Wever_Tornede_Hüllermeier, title={Predicting Machine Learning
Pipeline Runtimes in the Context of Automated Machine Learning}, journal={IEEE
Transactions on Pattern Analysis and Machine Intelligence}, publisher={IEEE},
author={Mohr, Felix and Wever, Marcel Dominik and Tornede, Alexander and Hüllermeier,
Eyke} }'
chicago: Mohr, Felix, Marcel Dominik Wever, Alexander Tornede, and Eyke Hüllermeier.
“Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine
Learning.” IEEE Transactions on Pattern Analysis and Machine Intelligence,
n.d.
ieee: F. Mohr, M. D. Wever, A. Tornede, and E. Hüllermeier, “Predicting Machine
Learning Pipeline Runtimes in the Context of Automated Machine Learning,” IEEE
Transactions on Pattern Analysis and Machine Intelligence.
mla: Mohr, Felix, et al. “Predicting Machine Learning Pipeline Runtimes in the Context
of Automated Machine Learning.” IEEE Transactions on Pattern Analysis and Machine
Intelligence, IEEE.
short: F. Mohr, M.D. Wever, A. Tornede, E. Hüllermeier, IEEE Transactions on Pattern
Analysis and Machine Intelligence (n.d.).
date_created: 2021-01-27T13:45:52Z
date_updated: 2022-01-06T06:54:45Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_status: accepted
publisher: IEEE
status: public
title: Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine
Learning
type: journal_article
user_id: '5786'
year: '2021'
...
---
_id: '21570'
author:
- first_name: Tanja
full_name: Tornede, Tanja
id: '40795'
last_name: Tornede
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Tornede T, Tornede A, Wever MD, Hüllermeier E. Coevolution of Remaining Useful
Lifetime Estimation Pipelines for Automated Predictive Maintenance. In: Proceedings
of the Genetic and Evolutionary Computation Conference. ; 2021.'
apa: Tornede, T., Tornede, A., Wever, M. D., & Hüllermeier, E. (2021). Coevolution
of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance.
Proceedings of the Genetic and Evolutionary Computation Conference. Genetic
and Evolutionary Computation Conference.
bibtex: '@inproceedings{Tornede_Tornede_Wever_Hüllermeier_2021, title={Coevolution
of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
author={Tornede, Tanja and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier,
Eyke}, year={2021} }'
chicago: Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, and Eyke Hüllermeier.
“Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive
Maintenance.” In Proceedings of the Genetic and Evolutionary Computation Conference,
2021.
ieee: T. Tornede, A. Tornede, M. D. Wever, and E. Hüllermeier, “Coevolution of Remaining
Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance,” presented
at the Genetic and Evolutionary Computation Conference, 2021.
mla: Tornede, Tanja, et al. “Coevolution of Remaining Useful Lifetime Estimation
Pipelines for Automated Predictive Maintenance.” Proceedings of the Genetic
and Evolutionary Computation Conference, 2021.
short: 'T. Tornede, A. Tornede, M.D. Wever, E. Hüllermeier, in: Proceedings of the
Genetic and Evolutionary Computation Conference, 2021.'
conference:
end_date: 2021-07-14
name: Genetic and Evolutionary Computation Conference
start_date: 2021-07-10
date_created: 2021-03-26T09:14:19Z
date_updated: 2022-01-06T06:55:06Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Proceedings of the Genetic and Evolutionary Computation Conference
status: public
title: Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated
Predictive Maintenance
type: conference
user_id: '5786'
year: '2021'
...
---
_id: '22913'
author:
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
citation:
ama: 'Hüllermeier E, Mohr F, Tornede A, Wever MD. Automated Machine Learning, Bounded
Rationality, and Rational Metareasoning. In: ; 2021.'
apa: Hüllermeier, E., Mohr, F., Tornede, A., & Wever, M. D. (2021). Automated
Machine Learning, Bounded Rationality, and Rational Metareasoning. ECML/PKDD
Workshop on Automating Data Science, Bilbao (Virtual).
bibtex: '@inproceedings{Hüllermeier_Mohr_Tornede_Wever_2021, title={Automated Machine
Learning, Bounded Rationality, and Rational Metareasoning}, author={Hüllermeier,
Eyke and Mohr, Felix and Tornede, Alexander and Wever, Marcel Dominik}, year={2021}
}'
chicago: Hüllermeier, Eyke, Felix Mohr, Alexander Tornede, and Marcel Dominik Wever.
“Automated Machine Learning, Bounded Rationality, and Rational Metareasoning,”
2021.
ieee: E. Hüllermeier, F. Mohr, A. Tornede, and M. D. Wever, “Automated Machine Learning,
Bounded Rationality, and Rational Metareasoning,” presented at the ECML/PKDD Workshop
on Automating Data Science, Bilbao (Virtual), 2021.
mla: Hüllermeier, Eyke, et al. Automated Machine Learning, Bounded Rationality,
and Rational Metareasoning. 2021.
short: 'E. Hüllermeier, F. Mohr, A. Tornede, M.D. Wever, in: 2021.'
conference:
end_date: 2021-09-17
location: Bilbao (Virtual)
name: ECML/PKDD Workshop on Automating Data Science
start_date: 2021-09-13
date_created: 2021-08-02T07:46:29Z
date_updated: 2022-01-06T06:55:43Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
quality_controlled: '1'
status: public
title: Automated Machine Learning, Bounded Rationality, and Rational Metareasoning
type: conference
user_id: '5786'
year: '2021'
...
---
_id: '30866'
abstract:
- lang: eng
text: "Automated machine learning (AutoML) strives for the automatic configuration\r\nof
machine learning algorithms and their composition into an overall (software)\r\nsolution
- a machine learning pipeline - tailored to the learning task\r\n(dataset) at
hand. Over the last decade, AutoML has developed into an\r\nindependent research
field with hundreds of contributions. While AutoML offers\r\nmany prospects, it
is also known to be quite resource-intensive, which is one\r\nof its major points
of criticism. The primary cause for a high resource\r\nconsumption is that many
approaches rely on the (costly) evaluation of many\r\nmachine learning pipelines
while searching for good candidates. This problem is\r\namplified in the context
of research on AutoML methods, due to large scale\r\nexperiments conducted with
many datasets and approaches, each of them being run\r\nwith several repetitions
to rule out random effects. In the spirit of recent\r\nwork on Green AI, this
paper is written in an attempt to raise the awareness of\r\nAutoML researchers
for the problem and to elaborate on possible remedies. To\r\nthis end, we identify
four categories of actions the community may take towards\r\nmore sustainable
research on AutoML, i.e. Green AutoML: design of AutoML\r\nsystems, benchmarking,
transparency and research incentives."
author:
- first_name: Tanja
full_name: Tornede, Tanja
id: '40795'
last_name: Tornede
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Jonas Manuel
full_name: Hanselle, Jonas Manuel
id: '43980'
last_name: Hanselle
orcid: 0000-0002-1231-4985
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Tornede T, Tornede A, Hanselle JM, Wever MD, Mohr F, Hüllermeier E. Towards
Green Automated Machine Learning: Status Quo and Future Directions. arXiv:211105850.
Published online 2021.'
apa: 'Tornede, T., Tornede, A., Hanselle, J. M., Wever, M. D., Mohr, F., & Hüllermeier,
E. (2021). Towards Green Automated Machine Learning: Status Quo and Future Directions.
In arXiv:2111.05850.'
bibtex: '@article{Tornede_Tornede_Hanselle_Wever_Mohr_Hüllermeier_2021, title={Towards
Green Automated Machine Learning: Status Quo and Future Directions}, journal={arXiv:2111.05850},
author={Tornede, Tanja and Tornede, Alexander and Hanselle, Jonas Manuel and Wever,
Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2021} }'
chicago: 'Tornede, Tanja, Alexander Tornede, Jonas Manuel Hanselle, Marcel Dominik
Wever, Felix Mohr, and Eyke Hüllermeier. “Towards Green Automated Machine Learning:
Status Quo and Future Directions.” ArXiv:2111.05850, 2021.'
ieee: 'T. Tornede, A. Tornede, J. M. Hanselle, M. D. Wever, F. Mohr, and E. Hüllermeier,
“Towards Green Automated Machine Learning: Status Quo and Future Directions,”
arXiv:2111.05850. 2021.'
mla: 'Tornede, Tanja, et al. “Towards Green Automated Machine Learning: Status Quo
and Future Directions.” ArXiv:2111.05850, 2021.'
short: T. Tornede, A. Tornede, J.M. Hanselle, M.D. Wever, F. Mohr, E. Hüllermeier,
ArXiv:2111.05850 (2021).
date_created: 2022-04-12T11:57:15Z
date_updated: 2022-04-12T12:01:23Z
department:
- _id: '34'
- _id: '7'
- _id: '26'
external_id:
arxiv:
- '2111.05850'
language:
- iso: eng
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '10'
name: 'SFB 901 - B2: SFB 901 - Subproject B2'
publication: arXiv:2111.05850
status: public
title: 'Towards Green Automated Machine Learning: Status Quo and Future Directions'
type: preprint
user_id: '38209'
year: '2021'
...
---
_id: '21198'
author:
- first_name: Jonas Manuel
full_name: Hanselle, Jonas Manuel
id: '43980'
last_name: Hanselle
orcid: 0000-0002-1231-4985
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Hanselle JM, Tornede A, Wever MD, Hüllermeier E. Algorithm Selection as Superset
Learning: Constructing Algorithm Selectors from Imprecise Performance Data. Published
online 2021.'
apa: 'Hanselle, J. M., Tornede, A., Wever, M. D., & Hüllermeier, E. (2021).
Algorithm Selection as Superset Learning: Constructing Algorithm Selectors
from Imprecise Performance Data. The 25th Pacific-Asia Conference on Knowledge
Discovery and Data Mining (PAKDD-2021), Delhi, India.'
bibtex: '@article{Hanselle_Tornede_Wever_Hüllermeier_2021, series={PAKDD}, title={Algorithm
Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise
Performance Data}, author={Hanselle, Jonas Manuel and Tornede, Alexander and Wever,
Marcel Dominik and Hüllermeier, Eyke}, year={2021}, collection={PAKDD} }'
chicago: 'Hanselle, Jonas Manuel, Alexander Tornede, Marcel Dominik Wever, and Eyke
Hüllermeier. “Algorithm Selection as Superset Learning: Constructing Algorithm
Selectors from Imprecise Performance Data.” PAKDD, 2021.'
ieee: 'J. M. Hanselle, A. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection
as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance
Data.” 2021.'
mla: 'Hanselle, Jonas Manuel, et al. Algorithm Selection as Superset Learning:
Constructing Algorithm Selectors from Imprecise Performance Data. 2021.'
short: J.M. Hanselle, A. Tornede, M.D. Wever, E. Hüllermeier, (2021).
conference:
end_date: 2021-05-14
location: Delhi, India
name: The 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021)
start_date: 2021-05-11
date_created: 2021-02-09T09:30:14Z
date_updated: 2022-08-24T12:49:06Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
series_title: PAKDD
status: public
title: 'Algorithm Selection as Superset Learning: Constructing Algorithm Selectors
from Imprecise Performance Data'
type: conference
user_id: '38209'
year: '2021'
...
---
_id: '17407'
author:
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Tornede A, Wever MD, Hüllermeier E. Extreme Algorithm Selection with Dyadic
Feature Representation. In: Discovery Science. ; 2020.'
apa: Tornede, A., Wever, M. D., & Hüllermeier, E. (2020). Extreme Algorithm
Selection with Dyadic Feature Representation. Discovery Science. Discovery
Science 2020.
bibtex: '@inproceedings{Tornede_Wever_Hüllermeier_2020, title={Extreme Algorithm
Selection with Dyadic Feature Representation}, booktitle={Discovery Science},
author={Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2020}
}'
chicago: Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Extreme
Algorithm Selection with Dyadic Feature Representation.” In Discovery Science,
2020.
ieee: A. Tornede, M. D. Wever, and E. Hüllermeier, “Extreme Algorithm Selection
with Dyadic Feature Representation,” presented at the Discovery Science 2020,
2020.
mla: Tornede, Alexander, et al. “Extreme Algorithm Selection with Dyadic Feature
Representation.” Discovery Science, 2020.
short: 'A. Tornede, M.D. Wever, E. Hüllermeier, in: Discovery Science, 2020.'
conference:
name: Discovery Science 2020
date_created: 2020-07-21T10:06:51Z
date_updated: 2022-01-06T06:53:10Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Discovery Science
status: public
title: Extreme Algorithm Selection with Dyadic Feature Representation
type: conference
user_id: '5786'
year: '2020'
...
---
_id: '17408'
author:
- first_name: Jonas Manuel
full_name: Hanselle, Jonas Manuel
id: '43980'
last_name: Hanselle
orcid: 0000-0002-1231-4985
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Hanselle JM, Tornede A, Wever MD, Hüllermeier E. Hybrid Ranking and Regression
for Algorithm Selection. In: KI 2020: Advances in Artificial Intelligence.
; 2020.'
apa: 'Hanselle, J. M., Tornede, A., Wever, M. D., & Hüllermeier, E. (2020).
Hybrid Ranking and Regression for Algorithm Selection. KI 2020: Advances in
Artificial Intelligence. 43rd German Conference on Artificial Intelligence.'
bibtex: '@inproceedings{Hanselle_Tornede_Wever_Hüllermeier_2020, title={Hybrid Ranking
and Regression for Algorithm Selection}, booktitle={KI 2020: Advances in Artificial
Intelligence}, author={Hanselle, Jonas Manuel and Tornede, Alexander and Wever,
Marcel Dominik and Hüllermeier, Eyke}, year={2020} }'
chicago: 'Hanselle, Jonas Manuel, Alexander Tornede, Marcel Dominik Wever, and Eyke
Hüllermeier. “Hybrid Ranking and Regression for Algorithm Selection.” In KI
2020: Advances in Artificial Intelligence, 2020.'
ieee: J. M. Hanselle, A. Tornede, M. D. Wever, and E. Hüllermeier, “Hybrid Ranking
and Regression for Algorithm Selection,” presented at the 43rd German Conference
on Artificial Intelligence, 2020.
mla: 'Hanselle, Jonas Manuel, et al. “Hybrid Ranking and Regression for Algorithm
Selection.” KI 2020: Advances in Artificial Intelligence, 2020.'
short: 'J.M. Hanselle, A. Tornede, M.D. Wever, E. Hüllermeier, in: KI 2020: Advances
in Artificial Intelligence, 2020.'
conference:
name: 43rd German Conference on Artificial Intelligence
date_created: 2020-07-21T10:21:09Z
date_updated: 2022-01-06T06:53:10Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: 'KI 2020: Advances in Artificial Intelligence'
status: public
title: Hybrid Ranking and Regression for Algorithm Selection
type: conference
user_id: '5786'
year: '2020'
...
---
_id: '17424'
author:
- first_name: Tanja
full_name: Tornede, Tanja
id: '40795'
last_name: Tornede
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Tornede T, Tornede A, Wever MD, Mohr F, Hüllermeier E. AutoML for Predictive
Maintenance: One Tool to RUL Them All. In: Proceedings of the ECMLPKDD 2020.
; 2020. doi:10.1007/978-3-030-66770-2_8'
apa: 'Tornede, T., Tornede, A., Wever, M. D., Mohr, F., & Hüllermeier, E. (2020).
AutoML for Predictive Maintenance: One Tool to RUL Them All. Proceedings of
the ECMLPKDD 2020. IOTStream Workshop @ ECMLPKDD 2020. https://doi.org/10.1007/978-3-030-66770-2_8'
bibtex: '@inproceedings{Tornede_Tornede_Wever_Mohr_Hüllermeier_2020, title={AutoML
for Predictive Maintenance: One Tool to RUL Them All}, DOI={10.1007/978-3-030-66770-2_8},
booktitle={Proceedings of the ECMLPKDD 2020}, author={Tornede, Tanja and Tornede,
Alexander and Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2020}
}'
chicago: 'Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, Felix Mohr, and
Eyke Hüllermeier. “AutoML for Predictive Maintenance: One Tool to RUL Them All.”
In Proceedings of the ECMLPKDD 2020, 2020. https://doi.org/10.1007/978-3-030-66770-2_8.'
ieee: 'T. Tornede, A. Tornede, M. D. Wever, F. Mohr, and E. Hüllermeier, “AutoML
for Predictive Maintenance: One Tool to RUL Them All,” presented at the IOTStream
Workshop @ ECMLPKDD 2020, 2020, doi: 10.1007/978-3-030-66770-2_8.'
mla: 'Tornede, Tanja, et al. “AutoML for Predictive Maintenance: One Tool to RUL
Them All.” Proceedings of the ECMLPKDD 2020, 2020, doi:10.1007/978-3-030-66770-2_8.'
short: 'T. Tornede, A. Tornede, M.D. Wever, F. Mohr, E. Hüllermeier, in: Proceedings
of the ECMLPKDD 2020, 2020.'
conference:
name: IOTStream Workshop @ ECMLPKDD 2020
date_created: 2020-07-28T09:17:41Z
date_updated: 2022-01-06T06:53:11Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
doi: 10.1007/978-3-030-66770-2_8
language:
- iso: eng
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '1'
name: SFB 901
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Proceedings of the ECMLPKDD 2020
status: public
title: 'AutoML for Predictive Maintenance: One Tool to RUL Them All'
type: conference
user_id: '5786'
year: '2020'
...
---
_id: '20306'
author:
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Tornede A, Wever MD, Hüllermeier E. Towards Meta-Algorithm Selection. In:
Workshop MetaLearn 2020 @ NeurIPS 2020. ; 2020.'
apa: Tornede, A., Wever, M. D., & Hüllermeier, E. (2020). Towards Meta-Algorithm
Selection. Workshop MetaLearn 2020 @ NeurIPS 2020. Workshop MetaLearn 2020
@ NeurIPS 2020, Online.
bibtex: '@inproceedings{Tornede_Wever_Hüllermeier_2020, title={Towards Meta-Algorithm
Selection}, booktitle={Workshop MetaLearn 2020 @ NeurIPS 2020}, author={Tornede,
Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2020} }'
chicago: Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Towards
Meta-Algorithm Selection.” In Workshop MetaLearn 2020 @ NeurIPS 2020, 2020.
ieee: A. Tornede, M. D. Wever, and E. Hüllermeier, “Towards Meta-Algorithm Selection,”
presented at the Workshop MetaLearn 2020 @ NeurIPS 2020, Online, 2020.
mla: Tornede, Alexander, et al. “Towards Meta-Algorithm Selection.” Workshop
MetaLearn 2020 @ NeurIPS 2020, 2020.
short: 'A. Tornede, M.D. Wever, E. Hüllermeier, in: Workshop MetaLearn 2020 @ NeurIPS
2020, 2020.'
conference:
location: Online
name: Workshop MetaLearn 2020 @ NeurIPS 2020
date_created: 2020-11-06T09:42:27Z
date_updated: 2022-01-06T06:54:26Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Workshop MetaLearn 2020 @ NeurIPS 2020
status: public
title: Towards Meta-Algorithm Selection
type: conference
user_id: '5786'
year: '2020'
...
---
_id: '18276'
abstract:
- lang: eng
text: "Algorithm selection (AS) deals with the automatic selection of an algorithm\r\nfrom
a fixed set of candidate algorithms most suitable for a specific instance\r\nof
an algorithmic problem class, where \"suitability\" often refers to an\r\nalgorithm's
runtime. Due to possibly extremely long runtimes of candidate\r\nalgorithms, training
data for algorithm selection models is usually generated\r\nunder time constraints
in the sense that not all algorithms are run to\r\ncompletion on all instances.
Thus, training data usually comprises censored\r\ninformation, as the true runtime
of algorithms timed out remains unknown.\r\nHowever, many standard AS approaches
are not able to handle such information in\r\na proper way. On the other side,
survival analysis (SA) naturally supports\r\ncensored data and offers appropriate
ways to use such data for learning\r\ndistributional models of algorithm runtime,
as we demonstrate in this work. We\r\nleverage such models as a basis of a sophisticated
decision-theoretic approach\r\nto algorithm selection, which we dub Run2Survive.
Moreover, taking advantage of\r\na framework of this kind, we advocate a risk-averse
approach to algorithm\r\nselection, in which the avoidance of a timeout is given
high priority. In an\r\nextensive experimental study with the standard benchmark
ASlib, our approach is\r\nshown to be highly competitive and in many cases even
superior to\r\nstate-of-the-art AS approaches."
author:
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Stefan
full_name: Werner, Stefan
last_name: Werner
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Tornede A, Wever MD, Werner S, Mohr F, Hüllermeier E. Run2Survive: A Decision-theoretic
Approach to Algorithm Selection based on Survival Analysis. In: ACML 2020.
; 2020.'
apa: 'Tornede, A., Wever, M. D., Werner, S., Mohr, F., & Hüllermeier, E. (2020).
Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival
Analysis. ACML 2020. 12th Asian Conference on Machine Learning, Bangkok,
Thailand.'
bibtex: '@inproceedings{Tornede_Wever_Werner_Mohr_Hüllermeier_2020, title={Run2Survive:
A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis},
booktitle={ACML 2020}, author={Tornede, Alexander and Wever, Marcel Dominik and
Werner, Stefan and Mohr, Felix and Hüllermeier, Eyke}, year={2020} }'
chicago: 'Tornede, Alexander, Marcel Dominik Wever, Stefan Werner, Felix Mohr, and
Eyke Hüllermeier. “Run2Survive: A Decision-Theoretic Approach to Algorithm Selection
Based on Survival Analysis.” In ACML 2020, 2020.'
ieee: 'A. Tornede, M. D. Wever, S. Werner, F. Mohr, and E. Hüllermeier, “Run2Survive:
A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis,”
presented at the 12th Asian Conference on Machine Learning, Bangkok, Thailand,
2020.'
mla: 'Tornede, Alexander, et al. “Run2Survive: A Decision-Theoretic Approach to
Algorithm Selection Based on Survival Analysis.” ACML 2020, 2020.'
short: 'A. Tornede, M.D. Wever, S. Werner, F. Mohr, E. Hüllermeier, in: ACML 2020,
2020.'
conference:
end_date: 2020-11-20
location: Bangkok, Thailand
name: 12th Asian Conference on Machine Learning
start_date: 2020-11-18
date_created: 2020-08-25T12:09:28Z
date_updated: 2022-01-06T06:53:28Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
main_file_link:
- url: https://arxiv.org/pdf/2007.02816.pdf
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: ACML 2020
status: public
title: 'Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on
Survival Analysis'
type: conference
user_id: '5786'
year: '2020'
...
---
_id: '15629'
abstract:
- lang: eng
text: In multi-label classification (MLC), each instance is associated with a set
of class labels, in contrast to standard classification where an instance is assigned
a single label. Binary relevance (BR) learning, which reduces a multi-label to
a set of binary classification problems, one per label, is arguably the most straight-forward
approach to MLC. In spite of its simplicity, BR proved to be competitive to more
sophisticated MLC methods, and still achieves state-of-the-art performance for
many loss functions. Somewhat surprisingly, the optimal choice of the base learner
for tackling the binary classification problems has received very little attention
so far. Taking advantage of the label independence assumption inherent to BR,
we propose a label-wise base learner selection method optimizing label-wise macro
averaged performance measures. In an extensive experimental evaluation, we find
that or approach, called LiBRe, can significantly improve generalization performance.
author:
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Wever MD, Tornede A, Mohr F, Hüllermeier E. LiBRe: Label-Wise Selection of
Base Learners in Binary Relevance for Multi-Label Classification. In: Springer.'
apa: 'Wever, M. D., Tornede, A., Mohr, F., & Hüllermeier, E. (n.d.). LiBRe:
Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification.
Symposium on Intelligent Data Analysis, Konstanz, Germany.'
bibtex: '@inproceedings{Wever_Tornede_Mohr_Hüllermeier, title={LiBRe: Label-Wise
Selection of Base Learners in Binary Relevance for Multi-Label Classification},
publisher={Springer}, author={Wever, Marcel Dominik and Tornede, Alexander and
Mohr, Felix and Hüllermeier, Eyke} }'
chicago: 'Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier.
“LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label
Classification.” Springer, n.d.'
ieee: 'M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “LiBRe: Label-Wise
Selection of Base Learners in Binary Relevance for Multi-Label Classification,”
presented at the Symposium on Intelligent Data Analysis, Konstanz, Germany.'
mla: 'Wever, Marcel Dominik, et al. LiBRe: Label-Wise Selection of Base Learners
in Binary Relevance for Multi-Label Classification. Springer.'
short: 'M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, in: Springer, n.d.'
conference:
end_date: 2020-04-27
location: Konstanz, Germany
name: Symposium on Intelligent Data Analysis
start_date: 2020-04-24
date_created: 2020-01-23T08:44:08Z
date_updated: 2022-01-06T06:52:30Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication_status: accepted
publisher: Springer
status: public
title: 'LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label
Classification'
type: conference
user_id: '5786'
year: '2020'
...
---
_id: '15025'
abstract:
- lang: eng
text: In software engineering, the imprecise requirements of a user are transformed
to a formal requirements specification during the requirements elicitation process.
This process is usually guided by requirements engineers interviewing the user.
We want to partially automate this first step of the software engineering process
in order to enable users to specify a desired software system on their own. With
our approach, users are only asked to provide exemplary behavioral descriptions.
The problem of synthesizing a requirements specification from examples can partially
be reduced to the problem of grammatical inference, to which we apply an active
coevolutionary learning approach. However, this approach would usually require
many feedback queries to be sent to the user. In this work, we extend and generalize
our active learning approach to receive knowledge from multiple oracles, also
known as proactive learning. The ‘user oracle’ represents input received from
the user and the ‘knowledge oracle’ represents available, formalized domain knowledge.
We call our two-oracle approach the ‘first apply knowledge then query’ (FAKT/Q)
algorithm. We compare FAKT/Q to the active learning approach and provide an extensive
benchmark evaluation. As result we find that the number of required user queries
is reduced and the inference process is sped up significantly. Finally, with so-called
On-The-Fly Markets, we present a motivation and an application of our approach
where such knowledge is available.
author:
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Lorijn
full_name: van Rooijen, Lorijn
id: '58843'
last_name: van Rooijen
- first_name: Heiko
full_name: Hamann, Heiko
last_name: Hamann
citation:
ama: Wever MD, van Rooijen L, Hamann H. Multi-Oracle Coevolutionary Learning of
Requirements Specifications from Examples in On-The-Fly Markets. Evolutionary
Computation. 2020;28(2):165–193. doi:10.1162/evco_a_00266
apa: Wever, M. D., van Rooijen, L., & Hamann, H. (2020). Multi-Oracle Coevolutionary
Learning of Requirements Specifications from Examples in On-The-Fly Markets. Evolutionary
Computation, 28(2), 165–193. https://doi.org/10.1162/evco_a_00266
bibtex: '@article{Wever_van Rooijen_Hamann_2020, title={Multi-Oracle Coevolutionary
Learning of Requirements Specifications from Examples in On-The-Fly Markets},
volume={28}, DOI={10.1162/evco_a_00266},
number={2}, journal={Evolutionary Computation}, publisher={MIT Press Journals},
author={Wever, Marcel Dominik and van Rooijen, Lorijn and Hamann, Heiko}, year={2020},
pages={165–193} }'
chicago: 'Wever, Marcel Dominik, Lorijn van Rooijen, and Heiko Hamann. “Multi-Oracle
Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly
Markets.” Evolutionary Computation 28, no. 2 (2020): 165–193. https://doi.org/10.1162/evco_a_00266.'
ieee: 'M. D. Wever, L. van Rooijen, and H. Hamann, “Multi-Oracle Coevolutionary
Learning of Requirements Specifications from Examples in On-The-Fly Markets,”
Evolutionary Computation, vol. 28, no. 2, pp. 165–193, 2020, doi: 10.1162/evco_a_00266.'
mla: Wever, Marcel Dominik, et al. “Multi-Oracle Coevolutionary Learning of Requirements
Specifications from Examples in On-The-Fly Markets.” Evolutionary Computation,
vol. 28, no. 2, MIT Press Journals, 2020, pp. 165–193, doi:10.1162/evco_a_00266.
short: M.D. Wever, L. van Rooijen, H. Hamann, Evolutionary Computation 28 (2020)
165–193.
date_created: 2019-11-18T14:19:19Z
date_updated: 2022-01-06T06:52:15Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
- _id: '63'
- _id: '238'
doi: 10.1162/evco_a_00266
intvolume: ' 28'
issue: '2'
language:
- iso: eng
page: 165–193
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '9'
name: SFB 901 - Subproject B1
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Evolutionary Computation
publication_status: published
publisher: MIT Press Journals
related_material:
link:
- relation: confirmation
url: https://www.mitpressjournals.org/doi/pdf/10.1162/evco_a_00266
status: public
title: Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples
in On-The-Fly Markets
type: journal_article
user_id: '15415'
volume: 28
year: '2020'
...
---
_id: '13770'
author:
- first_name: Holger
full_name: Karl, Holger
id: '126'
last_name: Karl
- first_name: Dennis
full_name: Kundisch, Dennis
id: '21117'
last_name: Kundisch
- first_name: Friedhelm
full_name: Meyer auf der Heide, Friedhelm
id: '15523'
last_name: Meyer auf der Heide
- first_name: Heike
full_name: Wehrheim, Heike
id: '573'
last_name: Wehrheim
citation:
ama: 'Karl H, Kundisch D, Meyer auf der Heide F, Wehrheim H. A Case for a New IT
Ecosystem: On-The-Fly Computing. Business & Information Systems Engineering.
2020;62(6):467-481. doi:10.1007/s12599-019-00627-x'
apa: 'Karl, H., Kundisch, D., Meyer auf der Heide, F., & Wehrheim, H. (2020).
A Case for a New IT Ecosystem: On-The-Fly Computing. Business & Information
Systems Engineering, 62(6), 467–481. https://doi.org/10.1007/s12599-019-00627-x'
bibtex: '@article{Karl_Kundisch_Meyer auf der Heide_Wehrheim_2020, title={A Case
for a New IT Ecosystem: On-The-Fly Computing}, volume={62}, DOI={10.1007/s12599-019-00627-x},
number={6}, journal={Business & Information Systems Engineering}, publisher={Springer},
author={Karl, Holger and Kundisch, Dennis and Meyer auf der Heide, Friedhelm and
Wehrheim, Heike}, year={2020}, pages={467–481} }'
chicago: 'Karl, Holger, Dennis Kundisch, Friedhelm Meyer auf der Heide, and Heike
Wehrheim. “A Case for a New IT Ecosystem: On-The-Fly Computing.” Business &
Information Systems Engineering 62, no. 6 (2020): 467–81. https://doi.org/10.1007/s12599-019-00627-x.'
ieee: 'H. Karl, D. Kundisch, F. Meyer auf der Heide, and H. Wehrheim, “A Case for
a New IT Ecosystem: On-The-Fly Computing,” Business & Information Systems
Engineering, vol. 62, no. 6, pp. 467–481, 2020, doi: 10.1007/s12599-019-00627-x.'
mla: 'Karl, Holger, et al. “A Case for a New IT Ecosystem: On-The-Fly Computing.”
Business & Information Systems Engineering, vol. 62, no. 6, Springer,
2020, pp. 467–81, doi:10.1007/s12599-019-00627-x.'
short: H. Karl, D. Kundisch, F. Meyer auf der Heide, H. Wehrheim, Business &
Information Systems Engineering 62 (2020) 467–481.
date_created: 2019-10-10T13:41:06Z
date_updated: 2022-12-02T09:27:17Z
ddc:
- '004'
department:
- _id: '276'
- _id: '75'
- _id: '63'
- _id: '77'
doi: 10.1007/s12599-019-00627-x
file:
- access_level: closed
content_type: application/pdf
creator: ups
date_created: 2019-12-12T10:24:47Z
date_updated: 2019-12-12T10:24:47Z
file_id: '15311'
file_name: Karl2019_Article_ACaseForANewITEcosystemOn-The-.pdf
file_size: 454532
relation: main_file
success: 1
file_date_updated: 2019-12-12T10:24:47Z
has_accepted_license: '1'
intvolume: ' 62'
issue: '6'
language:
- iso: eng
page: 467-481
project:
- _id: '1'
name: SFB 901
- _id: '2'
name: SFB 901 - Project Area A
- _id: '3'
name: SFB 901 - Project Area B
- _id: '4'
name: SFB 901 - Project Area C
- _id: '82'
name: SFB 901 - Project Area T
- _id: '5'
name: SFB 901 - Subproject A1
- _id: '6'
name: SFB 901 - Subproject A2
- _id: '7'
name: SFB 901 - Subproject A3
- _id: '8'
name: SFB 901 - Subproject A4
- _id: '9'
name: SFB 901 - Subproject B1
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '11'
name: SFB 901 - Subproject B3
- _id: '12'
name: SFB 901 - Subproject B4
- _id: '13'
name: SFB 901 - Subproject C1
- _id: '14'
name: SFB 901 - Subproject C2
- _id: '15'
name: SFB 901 - Subproject C3
- _id: '16'
name: SFB 901 - Subproject C4
- _id: '17'
name: SFB 901 - Subproject C5
- _id: '83'
name: SFB 901 -Subproject T1
- _id: '84'
name: SFB 901 -Subproject T2
- _id: '107'
name: SFB 901 -Subproject T3
- _id: '158'
name: 'SFB 901 - T4: SFB 901 -Subproject T4'
publication: Business & Information Systems Engineering
publication_status: published
publisher: Springer
status: public
title: 'A Case for a New IT Ecosystem: On-The-Fly Computing'
type: journal_article
user_id: '477'
volume: 62
year: '2020'
...
---
_id: '8868'
author:
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Alexander
full_name: Hetzer, Alexander
id: '38209'
last_name: Hetzer
citation:
ama: 'Wever MD, Mohr F, Hüllermeier E, Hetzer A. Towards Automated Machine Learning
for Multi-Label Classification. In: ; 2019.'
apa: Wever, M. D., Mohr, F., Hüllermeier, E., & Hetzer, A. (2019). Towards Automated
Machine Learning for Multi-Label Classification. Presented at the European Conference
on Data Analytics (ECDA), Bayreuth, Germany.
bibtex: '@inproceedings{Wever_Mohr_Hüllermeier_Hetzer_2019, title={Towards Automated
Machine Learning for Multi-Label Classification}, author={Wever, Marcel Dominik
and Mohr, Felix and Hüllermeier, Eyke and Hetzer, Alexander}, year={2019} }'
chicago: Wever, Marcel Dominik, Felix Mohr, Eyke Hüllermeier, and Alexander Hetzer.
“Towards Automated Machine Learning for Multi-Label Classification,” 2019.
ieee: M. D. Wever, F. Mohr, E. Hüllermeier, and A. Hetzer, “Towards Automated Machine
Learning for Multi-Label Classification,” presented at the European Conference
on Data Analytics (ECDA), Bayreuth, Germany, 2019.
mla: Wever, Marcel Dominik, et al. Towards Automated Machine Learning for Multi-Label
Classification. 2019.
short: 'M.D. Wever, F. Mohr, E. Hüllermeier, A. Hetzer, in: 2019.'
conference:
end_date: 2019-03-20
location: Bayreuth, Germany
name: European Conference on Data Analytics (ECDA)
start_date: 2019-03-18
date_created: 2019-04-10T07:17:55Z
date_updated: 2022-01-06T07:04:04Z
ddc:
- '000'
department:
- _id: '355'
file:
- access_level: closed
content_type: application/pdf
creator: wever
date_created: 2019-04-10T07:17:17Z
date_updated: 2019-04-10T07:17:17Z
file_id: '8870'
file_name: Towards_Automated_Machine_Learning_for_Multi_Label_Classification.pdf
file_size: '74484'
relation: main_file
success: 1
file_date_updated: 2019-04-10T07:17:17Z
has_accepted_license: '1'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
status: public
title: Towards Automated Machine Learning for Multi-Label Classification
type: conference_abstract
user_id: '49109'
year: '2019'
...
---
_id: '15007'
author:
- first_name: Vitaly
full_name: Melnikov, Vitaly
id: '58747'
last_name: Melnikov
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Melnikov V, Hüllermeier E. Learning to Aggregate: Tackling the Aggregation/Disaggregation
Problem for OWA. In: Proceedings ACML, Asian Conference on Machine Learning
(Proceedings of Machine Learning Research, 101). ; 2019. doi:10.1016/j.jmva.2019.02.017'
apa: 'Melnikov, V., & Hüllermeier, E. (2019). Learning to Aggregate: Tackling
the Aggregation/Disaggregation Problem for OWA. In Proceedings ACML, Asian
Conference on Machine Learning (Proceedings of Machine Learning Research, 101).
https://doi.org/10.1016/j.jmva.2019.02.017'
bibtex: '@inproceedings{Melnikov_Hüllermeier_2019, title={Learning to Aggregate:
Tackling the Aggregation/Disaggregation Problem for OWA}, DOI={10.1016/j.jmva.2019.02.017},
booktitle={Proceedings ACML, Asian Conference on Machine Learning (Proceedings
of Machine Learning Research, 101)}, author={Melnikov, Vitaly and Hüllermeier,
Eyke}, year={2019} }'
chicago: 'Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate: Tackling
the Aggregation/Disaggregation Problem for OWA.” In Proceedings ACML, Asian
Conference on Machine Learning (Proceedings of Machine Learning Research, 101),
2019. https://doi.org/10.1016/j.jmva.2019.02.017.'
ieee: 'V. Melnikov and E. Hüllermeier, “Learning to Aggregate: Tackling the Aggregation/Disaggregation
Problem for OWA,” in Proceedings ACML, Asian Conference on Machine Learning
(Proceedings of Machine Learning Research, 101), 2019.'
mla: 'Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate: Tackling the
Aggregation/Disaggregation Problem for OWA.” Proceedings ACML, Asian Conference
on Machine Learning (Proceedings of Machine Learning Research, 101), 2019,
doi:10.1016/j.jmva.2019.02.017.'
short: 'V. Melnikov, E. Hüllermeier, in: Proceedings ACML, Asian Conference on Machine
Learning (Proceedings of Machine Learning Research, 101), 2019.'
date_created: 2019-11-15T10:43:26Z
date_updated: 2022-01-06T06:52:14Z
ddc:
- '000'
department:
- _id: '34'
- _id: '355'
- _id: '7'
doi: 10.1016/j.jmva.2019.02.017
file:
- access_level: open_access
content_type: application/pdf
creator: lettmann
date_created: 2020-02-28T12:47:07Z
date_updated: 2020-02-28T12:47:07Z
file_id: '16156'
file_name: learning-to-aggregate-owa.pdf
file_size: 2331320
relation: main_file
file_date_updated: 2020-02-28T12:47:07Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '3'
name: SFB 901 - Project Area B
- _id: '1'
name: SFB 901
publication: Proceedings ACML, Asian Conference on Machine Learning (Proceedings of
Machine Learning Research, 101)
publication_status: published
status: public
title: 'Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for
OWA'
type: conference
user_id: '477'
year: '2019'
...
---
_id: '15011'
author:
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Tornede A, Wever MD, Hüllermeier E. Algorithm Selection as Recommendation:
From Collaborative Filtering to Dyad Ranking. In: Hoffmann F, Hüllermeier E, Mikut
R, eds. Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28.
- 29. November 2019. KIT Scientific Publishing, Karlsruhe; 2019:135-146.'
apa: 'Tornede, A., Wever, M. D., & Hüllermeier, E. (2019). Algorithm Selection
as Recommendation: From Collaborative Filtering to Dyad Ranking. In F. Hoffmann,
E. Hüllermeier, & R. Mikut (Eds.), Proceedings - 29. Workshop Computational
Intelligence, Dortmund, 28. - 29. November 2019 (pp. 135–146). Dortmund: KIT
Scientific Publishing, Karlsruhe.'
bibtex: '@inproceedings{Tornede_Wever_Hüllermeier_2019, title={Algorithm Selection
as Recommendation: From Collaborative Filtering to Dyad Ranking}, booktitle={Proceedings
- 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019},
publisher={KIT Scientific Publishing, Karlsruhe}, author={Tornede, Alexander and
Wever, Marcel Dominik and Hüllermeier, Eyke}, editor={Hoffmann, Frank and Hüllermeier,
Eyke and Mikut, RalfEditors}, year={2019}, pages={135–146} }'
chicago: 'Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Algorithm
Selection as Recommendation: From Collaborative Filtering to Dyad Ranking.” In
Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29.
November 2019, edited by Frank Hoffmann, Eyke Hüllermeier, and Ralf Mikut,
135–46. KIT Scientific Publishing, Karlsruhe, 2019.'
ieee: 'A. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection as Recommendation:
From Collaborative Filtering to Dyad Ranking,” in Proceedings - 29. Workshop
Computational Intelligence, Dortmund, 28. - 29. November 2019, Dortmund, 2019,
pp. 135–146.'
mla: 'Tornede, Alexander, et al. “Algorithm Selection as Recommendation: From Collaborative
Filtering to Dyad Ranking.” Proceedings - 29. Workshop Computational Intelligence,
Dortmund, 28. - 29. November 2019, edited by Frank Hoffmann et al., KIT Scientific
Publishing, Karlsruhe, 2019, pp. 135–46.'
short: 'A. Tornede, M.D. Wever, E. Hüllermeier, in: F. Hoffmann, E. Hüllermeier,
R. Mikut (Eds.), Proceedings - 29. Workshop Computational Intelligence, Dortmund,
28. - 29. November 2019, KIT Scientific Publishing, Karlsruhe, 2019, pp. 135–146.'
conference:
end_date: 2019-11-29
location: Dortmund
name: 29. Workshop Computational Intelligence
start_date: 2019-11-28
date_created: 2019-11-15T13:29:25Z
date_updated: 2022-01-06T06:52:14Z
ddc:
- '006'
department:
- _id: '355'
editor:
- first_name: Frank
full_name: Hoffmann, Frank
last_name: Hoffmann
- first_name: Eyke
full_name: Hüllermeier, Eyke
last_name: Hüllermeier
- first_name: Ralf
full_name: Mikut, Ralf
last_name: Mikut
file:
- access_level: open_access
content_type: application/pdf
creator: ahetzer
date_created: 2020-05-25T08:01:31Z
date_updated: 2020-05-25T08:01:31Z
file_id: '17060'
file_name: ci_workshop_tornede.pdf
file_size: 468825
relation: main_file
file_date_updated: 2020-05-25T08:01:31Z
has_accepted_license: '1'
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
oa: '1'
page: 135-146
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28.
- 29. November 2019
publication_identifier:
isbn:
- 978-3-7315-0979-0
publication_status: published
publisher: KIT Scientific Publishing, Karlsruhe
status: public
title: 'Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad
Ranking'
type: conference
user_id: '38209'
year: '2019'
...
---
_id: '13132'
author:
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Mohr F, Wever MD, Tornede A, Hüllermeier E. From Automated to On-The-Fly Machine
Learning. In: INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik
Für Gesellschaft. INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft
für Informatik. Bonn: Gesellschaft für Informatik e.V.; 2019:273-274.'
apa: 'Mohr, F., Wever, M. D., Tornede, A., & Hüllermeier, E. (2019). From Automated
to On-The-Fly Machine Learning. In INFORMATIK 2019: 50 Jahre Gesellschaft für
Informatik – Informatik für Gesellschaft (pp. 273–274). Bonn: Gesellschaft
für Informatik e.V.'
bibtex: '@inproceedings{Mohr_Wever_Tornede_Hüllermeier_2019, place={Bonn}, series={INFORMATIK
2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik}, title={From
Automated to On-The-Fly Machine Learning}, booktitle={INFORMATIK 2019: 50 Jahre
Gesellschaft für Informatik – Informatik für Gesellschaft}, publisher={Gesellschaft
für Informatik e.V.}, author={Mohr, Felix and Wever, Marcel Dominik and Tornede,
Alexander and Hüllermeier, Eyke}, year={2019}, pages={273–274}, collection={INFORMATIK
2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik} }'
chicago: 'Mohr, Felix, Marcel Dominik Wever, Alexander Tornede, and Eyke Hüllermeier.
“From Automated to On-The-Fly Machine Learning.” In INFORMATIK 2019: 50 Jahre
Gesellschaft Für Informatik – Informatik Für Gesellschaft, 273–74. INFORMATIK
2019, Lecture Notes in Informatics (LNI), Gesellschaft Für Informatik. Bonn: Gesellschaft
für Informatik e.V., 2019.'
ieee: 'F. Mohr, M. D. Wever, A. Tornede, and E. Hüllermeier, “From Automated to
On-The-Fly Machine Learning,” in INFORMATIK 2019: 50 Jahre Gesellschaft für
Informatik – Informatik für Gesellschaft, Kassel, 2019, pp. 273–274.'
mla: 'Mohr, Felix, et al. “From Automated to On-The-Fly Machine Learning.” INFORMATIK
2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft,
Gesellschaft für Informatik e.V., 2019, pp. 273–74.'
short: 'F. Mohr, M.D. Wever, A. Tornede, E. Hüllermeier, in: INFORMATIK 2019: 50
Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft, Gesellschaft
für Informatik e.V., Bonn, 2019, pp. 273–274.'
conference:
end_date: 2019-09-26
location: Kassel
name: Informatik 2019
start_date: 2019-09-23
date_created: 2019-09-04T08:44:46Z
date_updated: 2022-01-06T06:51:28Z
department:
- _id: '355'
language:
- iso: eng
page: ' 273-274 '
place: Bonn
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
publication: 'INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für
Gesellschaft'
publisher: Gesellschaft für Informatik e.V.
series_title: INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für
Informatik
status: public
title: From Automated to On-The-Fly Machine Learning
type: conference_abstract
user_id: '38209'
year: '2019'
...
---
_id: '10232'
abstract:
- lang: eng
text: Existing tools for automated machine learning, such as Auto-WEKA, TPOT, auto-sklearn,
and more recently ML-Plan, have shown impressive results for the tasks of single-label
classification and regression. Yet, there is only little work on other types of
machine learning problems so far. In particular, there is almost no work on automating
the engineering of machine learning solutions for multi-label classification (MLC).
We show how the scope of ML-Plan, an AutoML-tool for multi-class classification,
can be extended towards MLC using MEKA, which is a multi-label extension of the
well-known Java library WEKA. The resulting approach recursively refines MEKA's
multi-label classifiers, nesting other multi-label classifiers for meta algorithms
and single-label classifiers provided by WEKA as base learners. In our evaluation,
we find that the proposed approach yields strong results and performs significantly
better than a set of baselines we compare with.
author:
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Wever MD, Mohr F, Tornede A, Hüllermeier E. Automating Multi-Label Classification
Extending ML-Plan. In: ; 2019.'
apa: Wever, M. D., Mohr, F., Tornede, A., & Hüllermeier, E. (2019). Automating
Multi-Label Classification Extending ML-Plan. Presented at the 6th ICML Workshop
on Automated Machine Learning (AutoML 2019), Long Beach, CA, USA.
bibtex: '@inproceedings{Wever_Mohr_Tornede_Hüllermeier_2019, title={Automating Multi-Label
Classification Extending ML-Plan}, author={Wever, Marcel Dominik and Mohr, Felix
and Tornede, Alexander and Hüllermeier, Eyke}, year={2019} }'
chicago: Wever, Marcel Dominik, Felix Mohr, Alexander Tornede, and Eyke Hüllermeier.
“Automating Multi-Label Classification Extending ML-Plan,” 2019.
ieee: M. D. Wever, F. Mohr, A. Tornede, and E. Hüllermeier, “Automating Multi-Label
Classification Extending ML-Plan,” presented at the 6th ICML Workshop on Automated
Machine Learning (AutoML 2019), Long Beach, CA, USA, 2019.
mla: Wever, Marcel Dominik, et al. Automating Multi-Label Classification Extending
ML-Plan. 2019.
short: 'M.D. Wever, F. Mohr, A. Tornede, E. Hüllermeier, in: 2019.'
conference:
end_date: 2019-06-15
location: Long Beach, CA, USA
name: 6th ICML Workshop on Automated Machine Learning (AutoML 2019)
start_date: 2019-06-09
date_created: 2019-06-11T21:33:06Z
date_updated: 2022-01-06T06:50:33Z
ddc:
- '006'
department:
- _id: '355'
file:
- access_level: open_access
content_type: application/pdf
creator: wever
date_created: 2019-09-10T08:19:01Z
date_updated: 2019-09-10T08:20:44Z
file_id: '13177'
file_name: Automating_MultiLabel_Classification_Extending_ML-Plan.pdf
file_size: 388191
relation: main_file
file_date_updated: 2019-09-10T08:20:44Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
status: public
title: Automating Multi-Label Classification Extending ML-Plan
type: conference
user_id: '33176'
year: '2019'
...
---
_id: '2479'
author:
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Amin
full_name: Faez, Amin
last_name: Faez
citation:
ama: 'Mohr F, Wever MD, Hüllermeier E, Faez A. (WIP) Towards the Automated Composition
of Machine Learning Services. In: SCC. San Francisco, CA, USA: IEEE; 2018.
doi:10.1109/SCC.2018.00039'
apa: 'Mohr, F., Wever, M. D., Hüllermeier, E., & Faez, A. (2018). (WIP) Towards
the Automated Composition of Machine Learning Services. In SCC. San Francisco,
CA, USA: IEEE. https://doi.org/10.1109/SCC.2018.00039'
bibtex: '@inproceedings{Mohr_Wever_Hüllermeier_Faez_2018, place={San Francisco,
CA, USA}, title={(WIP) Towards the Automated Composition of Machine Learning Services},
DOI={10.1109/SCC.2018.00039},
booktitle={SCC}, publisher={IEEE}, author={Mohr, Felix and Wever, Marcel Dominik
and Hüllermeier, Eyke and Faez, Amin}, year={2018} }'
chicago: 'Mohr, Felix, Marcel Dominik Wever, Eyke Hüllermeier, and Amin Faez. “(WIP)
Towards the Automated Composition of Machine Learning Services.” In SCC.
San Francisco, CA, USA: IEEE, 2018. https://doi.org/10.1109/SCC.2018.00039.'
ieee: F. Mohr, M. D. Wever, E. Hüllermeier, and A. Faez, “(WIP) Towards the Automated
Composition of Machine Learning Services,” in SCC, San Francisco, CA, USA,
2018.
mla: Mohr, Felix, et al. “(WIP) Towards the Automated Composition of Machine Learning
Services.” SCC, IEEE, 2018, doi:10.1109/SCC.2018.00039.
short: 'F. Mohr, M.D. Wever, E. Hüllermeier, A. Faez, in: SCC, IEEE, San Francisco,
CA, USA, 2018.'
conference:
end_date: 2018-07-07
location: San Francisco, CA, USA
name: IEEE International Conference on Services Computing, SCC 2018
start_date: 2018-07-02
date_created: 2018-04-24T08:34:52Z
date_updated: 2022-01-06T06:56:35Z
ddc:
- '000'
department:
- _id: '355'
doi: 10.1109/SCC.2018.00039
file:
- access_level: closed
content_type: application/pdf
creator: wever
date_created: 2018-11-06T15:08:39Z
date_updated: 2018-11-06T15:08:39Z
file_id: '5382'
file_name: 08456425.pdf
file_size: 237890
relation: main_file
file_date_updated: 2018-11-06T15:08:39Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://ieeexplore.ieee.org/document/8456425
oa: '1'
place: San Francisco, CA, USA
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
publication: SCC
publication_status: published
publisher: IEEE
status: public
title: (WIP) Towards the Automated Composition of Machine Learning Services
type: conference
user_id: '49109'
year: '2018'
...
---
_id: '2857'
author:
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Theodor
full_name: Lettmann, Theodor
id: '315'
last_name: Lettmann
orcid: 0000-0001-5859-2457
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
citation:
ama: 'Mohr F, Lettmann T, Hüllermeier E, Wever MD. Programmatic Task Network Planning.
In: Proceedings of the 1st ICAPS Workshop on Hierarchical Planning. AAAI;
2018:31-39.'
apa: 'Mohr, F., Lettmann, T., Hüllermeier, E., & Wever, M. D. (2018). Programmatic
Task Network Planning. In Proceedings of the 1st ICAPS Workshop on Hierarchical
Planning (pp. 31–39). Delft, Netherlands: AAAI.'
bibtex: '@inproceedings{Mohr_Lettmann_Hüllermeier_Wever_2018, title={Programmatic
Task Network Planning}, booktitle={Proceedings of the 1st ICAPS Workshop on Hierarchical
Planning}, publisher={AAAI}, author={Mohr, Felix and Lettmann, Theodor and Hüllermeier,
Eyke and Wever, Marcel Dominik}, year={2018}, pages={31–39} }'
chicago: Mohr, Felix, Theodor Lettmann, Eyke Hüllermeier, and Marcel Dominik Wever.
“Programmatic Task Network Planning.” In Proceedings of the 1st ICAPS Workshop
on Hierarchical Planning, 31–39. AAAI, 2018.
ieee: F. Mohr, T. Lettmann, E. Hüllermeier, and M. D. Wever, “Programmatic Task
Network Planning,” in Proceedings of the 1st ICAPS Workshop on Hierarchical
Planning, Delft, Netherlands, 2018, pp. 31–39.
mla: Mohr, Felix, et al. “Programmatic Task Network Planning.” Proceedings of
the 1st ICAPS Workshop on Hierarchical Planning, AAAI, 2018, pp. 31–39.
short: 'F. Mohr, T. Lettmann, E. Hüllermeier, M.D. Wever, in: Proceedings of the
1st ICAPS Workshop on Hierarchical Planning, AAAI, 2018, pp. 31–39.'
conference:
end_date: 2018-06-29
location: Delft, Netherlands
name: 28th International Conference on Automated Planning and Scheduling
start_date: 2018-06-24
date_created: 2018-05-24T09:00:20Z
date_updated: 2022-01-06T06:58:08Z
ddc:
- '000'
department:
- _id: '355'
file:
- access_level: closed
content_type: application/pdf
creator: wever
date_created: 2018-11-06T15:18:26Z
date_updated: 2018-11-06T15:18:26Z
file_id: '5384'
file_name: Mohr18ProgrammaticPlanning.pdf
file_size: 349958
relation: main_file
success: 1
file_date_updated: 2018-11-06T15:18:26Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://icaps18.icaps-conference.org/fileadmin/alg/conferences/icaps18/workshops/workshop08/docs/Mohr18ProgrammaticPlanning.pdf
oa: '1'
page: 31-39
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
publication: Proceedings of the 1st ICAPS Workshop on Hierarchical Planning
publisher: AAAI
status: public
title: Programmatic Task Network Planning
type: conference
user_id: '315'
year: '2018'
...
---
_id: '2471'
author:
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Mohr F, Wever MD, Hüllermeier E. On-The-Fly Service Construction with Prototypes.
In: SCC. San Francisco, CA, USA: IEEE Computer Society; 2018. doi:10.1109/SCC.2018.00036'
apa: 'Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). On-The-Fly Service Construction
with Prototypes. In SCC. San Francisco, CA, USA: IEEE Computer Society.
https://doi.org/10.1109/SCC.2018.00036'
bibtex: '@inproceedings{Mohr_Wever_Hüllermeier_2018, place={San Francisco, CA, USA},
title={On-The-Fly Service Construction with Prototypes}, DOI={10.1109/SCC.2018.00036},
booktitle={SCC}, publisher={IEEE Computer Society}, author={Mohr, Felix and Wever,
Marcel Dominik and Hüllermeier, Eyke}, year={2018} }'
chicago: 'Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “On-The-Fly Service
Construction with Prototypes.” In SCC. San Francisco, CA, USA: IEEE Computer
Society, 2018. https://doi.org/10.1109/SCC.2018.00036.'
ieee: F. Mohr, M. D. Wever, and E. Hüllermeier, “On-The-Fly Service Construction
with Prototypes,” in SCC, San Francisco, CA, USA, 2018.
mla: Mohr, Felix, et al. “On-The-Fly Service Construction with Prototypes.” SCC,
IEEE Computer Society, 2018, doi:10.1109/SCC.2018.00036.
short: 'F. Mohr, M.D. Wever, E. Hüllermeier, in: SCC, IEEE Computer Society, San
Francisco, CA, USA, 2018.'
conference:
end_date: 2018-07-07
location: San Francisco, CA, USA
name: IEEE International Conference on Services Computing, SCC 2018
start_date: 2018-07-02
date_created: 2018-04-23T11:40:20Z
date_updated: 2022-01-06T06:56:32Z
ddc:
- '000'
department:
- _id: '355'
doi: 10.1109/SCC.2018.00036
file:
- access_level: closed
content_type: application/pdf
creator: wever
date_created: 2018-11-06T15:15:38Z
date_updated: 2018-11-06T15:15:38Z
file_id: '5383'
file_name: 08456422.pdf
file_size: 356132
relation: main_file
success: 1
file_date_updated: 2018-11-06T15:15:38Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://ieeexplore.ieee.org/abstract/document/8456422
oa: '1'
place: San Francisco, CA, USA
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
publication: SCC
publisher: IEEE Computer Society
status: public
title: On-The-Fly Service Construction with Prototypes
type: conference
user_id: '49109'
year: '2018'
...
---
_id: '3510'
abstract:
- lang: eng
text: Automated machine learning (AutoML) seeks to automatically select, compose,
and parametrize machine learning algorithms, so as to achieve optimal performance
on a given task (dataset). Although current approaches to AutoML have already
produced impressive results, the field is still far from mature, and new techniques
are still being developed. In this paper, we present ML-Plan, a new approach to
AutoML based on hierarchical planning. To highlight the potential of this approach,
we compare ML-Plan to the state-of-the-art frameworks Auto-WEKA, auto-sklearn,
and TPOT. In an extensive series of experiments, we show that ML-Plan is highly
competitive and often outperforms existing approaches.
article_type: original
author:
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Mohr F, Wever MD, Hüllermeier E. ML-Plan: Automated Machine Learning via Hierarchical
Planning. Machine Learning. Published online 2018:1495-1515. doi:10.1007/s10994-018-5735-z'
apa: 'Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). ML-Plan: Automated Machine
Learning via Hierarchical Planning. Machine Learning, 1495–1515. https://doi.org/10.1007/s10994-018-5735-z'
bibtex: '@article{Mohr_Wever_Hüllermeier_2018, title={ML-Plan: Automated Machine
Learning via Hierarchical Planning}, DOI={10.1007/s10994-018-5735-z},
journal={Machine Learning}, publisher={Springer}, author={Mohr, Felix and Wever,
Marcel Dominik and Hüllermeier, Eyke}, year={2018}, pages={1495–1515} }'
chicago: 'Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “ML-Plan: Automated
Machine Learning via Hierarchical Planning.” Machine Learning, 2018, 1495–1515.
https://doi.org/10.1007/s10994-018-5735-z.'
ieee: 'F. Mohr, M. D. Wever, and E. Hüllermeier, “ML-Plan: Automated Machine Learning
via Hierarchical Planning,” Machine Learning, pp. 1495–1515, 2018, doi:
10.1007/s10994-018-5735-z.'
mla: 'Mohr, Felix, et al. “ML-Plan: Automated Machine Learning via Hierarchical
Planning.” Machine Learning, Springer, 2018, pp. 1495–515, doi:10.1007/s10994-018-5735-z.'
short: F. Mohr, M.D. Wever, E. Hüllermeier, Machine Learning (2018) 1495–1515.
conference:
end_date: 2018-09-14
location: Dublin, Ireland
name: European Conference on Machine Learning and Principles and Practice of Knowledge
Discovery in Databases
start_date: 2018-09-10
date_created: 2018-07-08T14:06:14Z
date_updated: 2022-01-06T06:59:21Z
ddc:
- '000'
department:
- _id: '355'
- _id: '34'
- _id: '7'
- _id: '26'
doi: 10.1007/s10994-018-5735-z
file:
- access_level: closed
content_type: application/pdf
creator: ups
date_created: 2018-11-02T15:32:16Z
date_updated: 2018-11-02T15:32:16Z
file_id: '5306'
file_name: ML-PlanAutomatedMachineLearnin.pdf
file_size: 1070937
relation: main_file
success: 1
file_date_updated: 2018-11-02T15:32:16Z
has_accepted_license: '1'
keyword:
- AutoML
- Hierarchical Planning
- HTN planning
- ML-Plan
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://rdcu.be/3Nc2
oa: '1'
page: 1495-1515
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Machine Learning
publication_identifier:
eissn:
- 1573-0565
issn:
- 0885-6125
publication_status: epub_ahead
publisher: Springer
status: public
title: 'ML-Plan: Automated Machine Learning via Hierarchical Planning'
type: journal_article
user_id: '5786'
year: '2018'
...
---
_id: '3552'
author:
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Mohr F, Wever MD, Hüllermeier E. Reduction Stumps for Multi-Class Classification.
In: Proceedings of the Symposium on Intelligent Data Analysis. ‘s-Hertogenbosch,
the Netherlands. doi:10.1007/978-3-030-01768-2_19'
apa: Mohr, F., Wever, M. D., & Hüllermeier, E. (n.d.). Reduction Stumps for
Multi-Class Classification. In Proceedings of the Symposium on Intelligent
Data Analysis. ‘s-Hertogenbosch, the Netherlands. https://doi.org/10.1007/978-3-030-01768-2_19
bibtex: '@inproceedings{Mohr_Wever_Hüllermeier, place={‘s-Hertogenbosch, the Netherlands},
title={Reduction Stumps for Multi-Class Classification}, DOI={10.1007/978-3-030-01768-2_19},
booktitle={Proceedings of the Symposium on Intelligent Data Analysis}, author={Mohr,
Felix and Wever, Marcel Dominik and Hüllermeier, Eyke} }'
chicago: Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “Reduction Stumps
for Multi-Class Classification.” In Proceedings of the Symposium on Intelligent
Data Analysis. ‘s-Hertogenbosch, the Netherlands, n.d. https://doi.org/10.1007/978-3-030-01768-2_19.
ieee: F. Mohr, M. D. Wever, and E. Hüllermeier, “Reduction Stumps for Multi-Class
Classification,” in Proceedings of the Symposium on Intelligent Data Analysis,
‘s-Hertogenbosch, the Netherlands.
mla: Mohr, Felix, et al. “Reduction Stumps for Multi-Class Classification.” Proceedings
of the Symposium on Intelligent Data Analysis, doi:10.1007/978-3-030-01768-2_19.
short: 'F. Mohr, M.D. Wever, E. Hüllermeier, in: Proceedings of the Symposium on
Intelligent Data Analysis, ‘s-Hertogenbosch, the Netherlands, n.d.'
conference:
end_date: 2018-10-26
location: ‘s-Hertogenbosch, the Netherlands
name: Symposium on Intelligent Data Analysis
start_date: 2018-10-24
date_created: 2018-07-13T15:29:15Z
date_updated: 2022-01-06T06:59:25Z
ddc:
- '000'
department:
- _id: '355'
doi: 10.1007/978-3-030-01768-2_19
file:
- access_level: closed
content_type: application/pdf
creator: wever
date_created: 2018-11-06T15:23:02Z
date_updated: 2018-11-06T15:23:02Z
file_id: '5385'
file_name: Mohr2018_Chapter_ReductionStumpsForMulti-classC.pdf
file_size: 1348768
relation: main_file
success: 1
file_date_updated: 2018-11-06T15:23:02Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://link.springer.com/chapter/10.1007%2F978-3-030-01768-2_19
oa: '1'
place: ‘s-Hertogenbosch, the Netherlands
project:
- _id: '1'
name: SFB 901
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '3'
name: SFB 901 - Project Area B
publication: Proceedings of the Symposium on Intelligent Data Analysis
publication_status: accepted
quality_controlled: '1'
status: public
title: Reduction Stumps for Multi-Class Classification
type: conference
user_id: '49109'
year: '2018'
...
---
_id: '3852'
abstract:
- lang: eng
text: "In automated machine learning (AutoML), the process of engineering machine
learning applications with respect to a specific problem is (partially) automated.\r\nVarious
AutoML tools have already been introduced to provide out-of-the-box machine learning
functionality.\r\nMore specifically, by selecting machine learning algorithms
and optimizing their hyperparameters, these tools produce a machine learning pipeline
tailored to the problem at hand.\r\nExcept for TPOT, all of these tools restrict
the maximum number of processing steps of such a pipeline.\r\nHowever, as TPOT
follows an evolutionary approach, it suffers from performance issues when dealing
with larger datasets.\r\nIn this paper, we present an alternative approach leveraging
a hierarchical planning to configure machine learning pipelines that are unlimited
in length.\r\nWe evaluate our approach and find its performance to be competitive
with other AutoML tools, including TPOT."
author:
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Wever MD, Mohr F, Hüllermeier E. ML-Plan for Unlimited-Length Machine Learning
Pipelines. In: ICML 2018 AutoML Workshop. ; 2018.'
apa: Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). ML-Plan for Unlimited-Length
Machine Learning Pipelines. In ICML 2018 AutoML Workshop. Stockholm, Sweden.
bibtex: '@inproceedings{Wever_Mohr_Hüllermeier_2018, title={ML-Plan for Unlimited-Length
Machine Learning Pipelines}, booktitle={ICML 2018 AutoML Workshop}, author={Wever,
Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2018} }'
chicago: Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “ML-Plan for Unlimited-Length
Machine Learning Pipelines.” In ICML 2018 AutoML Workshop, 2018.
ieee: M. D. Wever, F. Mohr, and E. Hüllermeier, “ML-Plan for Unlimited-Length Machine
Learning Pipelines,” in ICML 2018 AutoML Workshop, Stockholm, Sweden, 2018.
mla: Wever, Marcel Dominik, et al. “ML-Plan for Unlimited-Length Machine Learning
Pipelines.” ICML 2018 AutoML Workshop, 2018.
short: 'M.D. Wever, F. Mohr, E. Hüllermeier, in: ICML 2018 AutoML Workshop, 2018.'
conference:
end_date: 2018-07-15
location: Stockholm, Sweden
name: ICML 2018 AutoML Workshop
start_date: 2018-07-10
date_created: 2018-08-09T06:14:54Z
date_updated: 2022-01-06T06:59:46Z
ddc:
- '006'
department:
- _id: '355'
file:
- access_level: open_access
content_type: application/pdf
creator: wever
date_created: 2018-08-09T06:14:43Z
date_updated: 2018-08-09T06:14:43Z
file_id: '3853'
file_name: 38.pdf
file_size: 297811
relation: main_file
file_date_updated: 2018-08-09T06:14:43Z
has_accepted_license: '1'
keyword:
- automated machine learning
- complex pipelines
- hierarchical planning
language:
- iso: eng
main_file_link:
- url: https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxhdXRvbWwyMDE4aWNtbHxneDo3M2Q3MjUzYjViNDRhZTAx
oa: '1'
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
publication: ICML 2018 AutoML Workshop
quality_controlled: '1'
status: public
title: ML-Plan for Unlimited-Length Machine Learning Pipelines
type: conference
urn: '38527'
user_id: '49109'
year: '2018'
...
---
_id: '2109'
abstract:
- lang: eng
text: In multinomial classification, reduction techniques are commonly used to decompose
the original learning problem into several simpler problems. For example, by recursively
bisecting the original set of classes, so-called nested dichotomies define a set
of binary classification problems that are organized in the structure of a binary
tree. In contrast to the existing one-shot heuristics for constructing nested
dichotomies and motivated by recent work on algorithm configuration, we propose
a genetic algorithm for optimizing the structure of such dichotomies. A key component
of this approach is the proposed genetic representation that facilitates the application
of standard genetic operators, while still supporting the exchange of partial
solutions under recombination. We evaluate the approach in an extensive experimental
study, showing that it yields classifiers with superior generalization performance.
author:
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Wever MD, Mohr F, Hüllermeier E. Ensembles of Evolved Nested Dichotomies for
Classification. In: Proceedings of the Genetic and Evolutionary Computation
Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018. Kyoto, Japan: ACM;
2018. doi:10.1145/3205455.3205562'
apa: 'Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). Ensembles of Evolved
Nested Dichotomies for Classification. In Proceedings of the Genetic and Evolutionary
Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018. Kyoto,
Japan: ACM. https://doi.org/10.1145/3205455.3205562'
bibtex: '@inproceedings{Wever_Mohr_Hüllermeier_2018, place={Kyoto, Japan}, title={Ensembles
of Evolved Nested Dichotomies for Classification}, DOI={10.1145/3205455.3205562},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference,
GECCO 2018, Kyoto, Japan, July 15-19, 2018}, publisher={ACM}, author={Wever, Marcel
Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2018} }'
chicago: 'Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Ensembles of
Evolved Nested Dichotomies for Classification.” In Proceedings of the Genetic
and Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19,
2018. Kyoto, Japan: ACM, 2018. https://doi.org/10.1145/3205455.3205562.'
ieee: M. D. Wever, F. Mohr, and E. Hüllermeier, “Ensembles of Evolved Nested Dichotomies
for Classification,” in Proceedings of the Genetic and Evolutionary Computation
Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018, Kyoto, Japan, 2018.
mla: Wever, Marcel Dominik, et al. “Ensembles of Evolved Nested Dichotomies for
Classification.” Proceedings of the Genetic and Evolutionary Computation Conference,
GECCO 2018, Kyoto, Japan, July 15-19, 2018, ACM, 2018, doi:10.1145/3205455.3205562.
short: 'M.D. Wever, F. Mohr, E. Hüllermeier, in: Proceedings of the Genetic and
Evolutionary Computation Conference, GECCO 2018, Kyoto, Japan, July 15-19, 2018,
ACM, Kyoto, Japan, 2018.'
conference:
end_date: 2018-07-19
location: Kyoto, Japan
name: GECCO 2018
start_date: 2018-07-15
date_created: 2018-03-31T13:51:23Z
date_updated: 2022-01-06T06:54:45Z
ddc:
- '000'
department:
- _id: '355'
doi: 10.1145/3205455.3205562
file:
- access_level: closed
content_type: application/pdf
creator: ups
date_created: 2018-11-02T14:33:54Z
date_updated: 2018-11-02T14:33:54Z
file_id: '5275'
file_name: p561-wever.pdf
file_size: 875404
relation: main_file
success: 1
file_date_updated: 2018-11-02T14:33:54Z
has_accepted_license: '1'
keyword:
- Classification
- Hierarchical Decomposition
- Indirect Encoding
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://dl.acm.org/citation.cfm?doid=3205455.3205562
oa: '1'
place: Kyoto, Japan
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO
2018, Kyoto, Japan, July 15-19, 2018
publication_status: published
publisher: ACM
status: public
title: Ensembles of Evolved Nested Dichotomies for Classification
type: conference
user_id: '33176'
year: '2018'
...
---
_id: '17713'
author:
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Wever MD, Mohr F, Hüllermeier E. Automated Multi-Label Classification based
on ML-Plan. Published online 2018.
apa: Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). Automated Multi-Label
Classification based on ML-Plan. Arxiv.
bibtex: '@article{Wever_Mohr_Hüllermeier_2018, title={Automated Multi-Label Classification
based on ML-Plan}, publisher={Arxiv}, author={Wever, Marcel Dominik and Mohr,
Felix and Hüllermeier, Eyke}, year={2018} }'
chicago: Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Automated Multi-Label
Classification Based on ML-Plan.” Arxiv, 2018.
ieee: M. D. Wever, F. Mohr, and E. Hüllermeier, “Automated Multi-Label Classification
based on ML-Plan.” Arxiv, 2018.
mla: Wever, Marcel Dominik, et al. Automated Multi-Label Classification Based
on ML-Plan. Arxiv, 2018.
short: M.D. Wever, F. Mohr, E. Hüllermeier, (2018).
date_created: 2020-08-07T11:38:10Z
date_updated: 2022-01-06T06:53:17Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/pdf/1811.04060.pdf
oa: '1'
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publisher: Arxiv
status: public
title: Automated Multi-Label Classification based on ML-Plan
type: preprint
user_id: '5786'
year: '2018'
...
---
_id: '17714'
author:
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Mohr F, Wever MD, Hüllermeier E. Automated machine learning service composition.
Published online 2018.
apa: Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). Automated machine
learning service composition.
bibtex: '@article{Mohr_Wever_Hüllermeier_2018, title={Automated machine learning
service composition}, author={Mohr, Felix and Wever, Marcel Dominik and Hüllermeier,
Eyke}, year={2018} }'
chicago: Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “Automated Machine
Learning Service Composition,” 2018.
ieee: F. Mohr, M. D. Wever, and E. Hüllermeier, “Automated machine learning service
composition.” 2018.
mla: Mohr, Felix, et al. Automated Machine Learning Service Composition.
2018.
short: F. Mohr, M.D. Wever, E. Hüllermeier, (2018).
date_created: 2020-08-07T11:40:13Z
date_updated: 2022-01-06T06:53:17Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/pdf/1809.00486.pdf
oa: '1'
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
status: public
title: Automated machine learning service composition
type: preprint
user_id: '5786'
year: '2018'
...
---
_id: '6423'
author:
- first_name: Dirk
full_name: Schäfer, Dirk
last_name: Schäfer
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Schäfer D, Hüllermeier E. Preference-Based Reinforcement Learning Using Dyad
Ranking. In: Discovery Science. Cham: Springer International Publishing;
2018:161-175. doi:10.1007/978-3-030-01771-2_11'
apa: 'Schäfer, D., & Hüllermeier, E. (2018). Preference-Based Reinforcement
Learning Using Dyad Ranking. In Discovery Science (pp. 161–175). Cham:
Springer International Publishing. https://doi.org/10.1007/978-3-030-01771-2_11'
bibtex: '@inbook{Schäfer_Hüllermeier_2018, place={Cham}, title={Preference-Based
Reinforcement Learning Using Dyad Ranking}, DOI={10.1007/978-3-030-01771-2_11},
booktitle={Discovery Science}, publisher={Springer International Publishing},
author={Schäfer, Dirk and Hüllermeier, Eyke}, year={2018}, pages={161–175} }'
chicago: 'Schäfer, Dirk, and Eyke Hüllermeier. “Preference-Based Reinforcement Learning
Using Dyad Ranking.” In Discovery Science, 161–75. Cham: Springer International
Publishing, 2018. https://doi.org/10.1007/978-3-030-01771-2_11.'
ieee: 'D. Schäfer and E. Hüllermeier, “Preference-Based Reinforcement Learning Using
Dyad Ranking,” in Discovery Science, Cham: Springer International Publishing,
2018, pp. 161–175.'
mla: Schäfer, Dirk, and Eyke Hüllermeier. “Preference-Based Reinforcement Learning
Using Dyad Ranking.” Discovery Science, Springer International Publishing,
2018, pp. 161–75, doi:10.1007/978-3-030-01771-2_11.
short: 'D. Schäfer, E. Hüllermeier, in: Discovery Science, Springer International
Publishing, Cham, 2018, pp. 161–175.'
date_created: 2018-12-20T15:52:03Z
date_updated: 2022-01-06T07:03:04Z
ddc:
- '000'
department:
- _id: '355'
doi: 10.1007/978-3-030-01771-2_11
file:
- access_level: closed
content_type: application/pdf
creator: ups
date_created: 2019-01-11T11:03:50Z
date_updated: 2019-01-11T11:03:50Z
file_id: '6623'
file_name: Schäfer-Hüllermeier2018_Chapter_Preference-BasedReinforcementL.pdf
file_size: 458972
relation: main_file
success: 1
file_date_updated: 2019-01-11T11:03:50Z
has_accepted_license: '1'
language:
- iso: eng
page: 161-175
place: Cham
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
publication: Discovery Science
publication_identifier:
isbn:
- '9783030017705'
- '9783030017712'
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer International Publishing
status: public
title: Preference-Based Reinforcement Learning Using Dyad Ranking
type: book_chapter
user_id: '49109'
year: '2018'
...
---
_id: '115'
abstract:
- lang: eng
text: 'Whenever customers have to decide between different instances of the same
product, they are interested in buying the best product. In contrast, companies
are interested in reducing the construction effort (and usually as a consequence
thereof, the quality) to gain profit. The described setting is widely known as
opposed preferences in quality of the product and also applies to the context
of service-oriented computing. In general, service-oriented computing emphasizes
the construction of large software systems out of existing services, where services
are small and self-contained pieces of software that adhere to a specified interface.
Several implementations of the same interface are considered as several instances
of the same service. Thereby, customers are interested in buying the best service
implementation for their service composition wrt. to metrics, such as costs, energy,
memory consumption, or execution time. One way to ensure the service quality is
to employ certificates, which can come in different kinds: Technical certificates
proving correctness can be automatically constructed by the service provider and
again be automatically checked by the user. Digital certificates allow proof of
the integrity of a product. Other certificates might be rolled out if service
providers follow a good software construction principle, which is checked in annual
audits. Whereas all of these certificates are handled differently in service markets,
what they have in common is that they influence the buying decisions of customers.
In this paper, we review state-of-the-art developments in certification with respect
to service-oriented computing. We not only discuss how certificates are constructed
and handled in service-oriented computing but also review the effects of certificates
on the market from an economic perspective.'
author:
- first_name: Marie-Christine
full_name: Jakobs, Marie-Christine
last_name: Jakobs
- first_name: Julia
full_name: Krämer, Julia
last_name: Krämer
- first_name: Dirk
full_name: van Straaten, Dirk
id: '10311'
last_name: van Straaten
- first_name: Theodor
full_name: Lettmann, Theodor
id: '315'
last_name: Lettmann
orcid: 0000-0001-5859-2457
citation:
ama: 'Jakobs M-C, Krämer J, van Straaten D, Lettmann T. Certification Matters for
Service Markets. In: Marcelo De Barros, Janusz Klink,Tadeus Uhl TP, ed. The
Ninth International Conferences on Advanced Service Computing (SERVICE COMPUTATION).
; 2017:7-12.'
apa: Jakobs, M.-C., Krämer, J., van Straaten, D., & Lettmann, T. (2017). Certification
Matters for Service Markets. In T. P. Marcelo De Barros, Janusz Klink,Tadeus Uhl
(Ed.), The Ninth International Conferences on Advanced Service Computing (SERVICE
COMPUTATION) (pp. 7–12).
bibtex: '@inproceedings{Jakobs_Krämer_van Straaten_Lettmann_2017, title={Certification
Matters for Service Markets}, booktitle={The Ninth International Conferences on
Advanced Service Computing (SERVICE COMPUTATION)}, author={Jakobs, Marie-Christine
and Krämer, Julia and van Straaten, Dirk and Lettmann, Theodor}, editor={Marcelo
De Barros, Janusz Klink,Tadeus Uhl, Thomas PrinzEditor}, year={2017}, pages={7–12}
}'
chicago: Jakobs, Marie-Christine, Julia Krämer, Dirk van Straaten, and Theodor Lettmann.
“Certification Matters for Service Markets.” In The Ninth International Conferences
on Advanced Service Computing (SERVICE COMPUTATION), edited by Thomas Prinz
Marcelo De Barros, Janusz Klink,Tadeus Uhl, 7–12, 2017.
ieee: M.-C. Jakobs, J. Krämer, D. van Straaten, and T. Lettmann, “Certification Matters
for Service Markets,” in The Ninth International Conferences on Advanced Service
Computing (SERVICE COMPUTATION), 2017, pp. 7–12.
mla: Jakobs, Marie-Christine, et al. “Certification Matters for Service Markets.”
The Ninth International Conferences on Advanced Service Computing (SERVICE
COMPUTATION), edited by Thomas Prinz Marcelo De Barros, Janusz Klink,Tadeus
Uhl, 2017, pp. 7–12.
short: 'M.-C. Jakobs, J. Krämer, D. van Straaten, T. Lettmann, in: T.P. Marcelo
De Barros, Janusz Klink,Tadeus Uhl (Ed.), The Ninth International Conferences
on Advanced Service Computing (SERVICE COMPUTATION), 2017, pp. 7–12.'
date_created: 2017-10-17T12:41:14Z
date_updated: 2022-01-06T06:51:02Z
ddc:
- '040'
department:
- _id: '77'
- _id: '355'
- _id: '179'
editor:
- first_name: Thomas Prinz
full_name: Marcelo De Barros, Janusz Klink,Tadeus Uhl, Thomas Prinz
last_name: Marcelo De Barros, Janusz Klink,Tadeus Uhl
file:
- access_level: closed
content_type: application/pdf
creator: florida
date_created: 2018-03-21T13:04:12Z
date_updated: 2018-03-21T13:04:12Z
file_id: '1564'
file_name: 115-JakobsKraemerVanStraatenLettmann2017.pdf
file_size: 133531
relation: main_file
success: 1
file_date_updated: 2018-03-21T13:04:12Z
has_accepted_license: '1'
language:
- iso: eng
page: 7-12
project:
- _id: '1'
name: SFB 901
- _id: '10'
name: SFB 901 - Subprojekt B2
- _id: '11'
name: SFB 901 - Subproject B3
- _id: '12'
name: SFB 901 - Subproject B4
- _id: '8'
name: SFB 901 - Subproject A4
- _id: '2'
name: SFB 901 - Project Area A
- _id: '3'
name: SFB 901 - Project Area B
publication: The Ninth International Conferences on Advanced Service Computing (SERVICE
COMPUTATION)
status: public
title: Certification Matters for Service Markets
type: conference
user_id: '477'
year: '2017'
...
---
_id: '71'
abstract:
- lang: eng
text: Today, software verification tools have reached the maturity to be used for
large scale programs. Different tools perform differently well on varying code.
A software developer is hence faced with the problem of choosing a tool appropriate
for her program at hand. A ranking of tools on programs could facilitate the choice.
Such rankings can, however, so far only be obtained by running all considered
tools on the program.In this paper, we present a machine learning approach to
predicting rankings of tools on programs. The method builds upon so-called label
ranking algorithms, which we complement with appropriate kernels providing a similarity
measure for programs. Our kernels employ a graph representation for software source
code that mixes elements of control flow and program dependence graphs with abstract
syntax trees. Using data sets from the software verification competition SV-COMP,
we demonstrate our rank prediction technique to generalize well and achieve a
rather high predictive accuracy (rank correlation > 0.6).
author:
- first_name: Mike
full_name: Czech, Mike
last_name: Czech
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Marie-Christine
full_name: Jakobs, Marie-Christine
last_name: Jakobs
- first_name: Heike
full_name: Wehrheim, Heike
id: '573'
last_name: Wehrheim
citation:
ama: 'Czech M, Hüllermeier E, Jakobs M-C, Wehrheim H. Predicting Rankings of Software
Verification Tools. In: Proceedings of the 3rd International Workshop on Software
Analytics. SWAN’17. ; 2017:23-26. doi:10.1145/3121257.3121262'
apa: Czech, M., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (2017). Predicting
Rankings of Software Verification Tools. In Proceedings of the 3rd International
Workshop on Software Analytics (pp. 23–26). https://doi.org/10.1145/3121257.3121262
bibtex: '@inproceedings{Czech_Hüllermeier_Jakobs_Wehrheim_2017, series={SWAN’17},
title={Predicting Rankings of Software Verification Tools}, DOI={10.1145/3121257.3121262},
booktitle={Proceedings of the 3rd International Workshop on Software Analytics},
author={Czech, Mike and Hüllermeier, Eyke and Jakobs, Marie-Christine and Wehrheim,
Heike}, year={2017}, pages={23–26}, collection={SWAN’17} }'
chicago: Czech, Mike, Eyke Hüllermeier, Marie-Christine Jakobs, and Heike Wehrheim.
“Predicting Rankings of Software Verification Tools.” In Proceedings of the
3rd International Workshop on Software Analytics, 23–26. SWAN’17, 2017. https://doi.org/10.1145/3121257.3121262.
ieee: M. Czech, E. Hüllermeier, M.-C. Jakobs, and H. Wehrheim, “Predicting Rankings
of Software Verification Tools,” in Proceedings of the 3rd International Workshop
on Software Analytics, 2017, pp. 23–26.
mla: Czech, Mike, et al. “Predicting Rankings of Software Verification Tools.” Proceedings
of the 3rd International Workshop on Software Analytics, 2017, pp. 23–26,
doi:10.1145/3121257.3121262.
short: 'M. Czech, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, in: Proceedings of
the 3rd International Workshop on Software Analytics, 2017, pp. 23–26.'
date_created: 2017-10-17T12:41:05Z
date_updated: 2022-01-06T07:03:28Z
ddc:
- '000'
department:
- _id: '355'
- _id: '77'
doi: 10.1145/3121257.3121262
file:
- access_level: closed
content_type: application/pdf
creator: ups
date_created: 2018-11-02T14:24:29Z
date_updated: 2018-11-02T14:24:29Z
file_id: '5271'
file_name: fsews17swan-swanmain1.pdf
file_size: 822383
relation: main_file
success: 1
file_date_updated: 2018-11-02T14:24:29Z
has_accepted_license: '1'
language:
- iso: eng
page: 23-26
project:
- _id: '1'
name: SFB 901
- _id: '12'
name: SFB 901 - Subprojekt B4
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '3'
name: SFB 901 - Project Area B
- _id: '11'
name: SFB 901 - Subproject B3
publication: Proceedings of the 3rd International Workshop on Software Analytics
series_title: SWAN'17
status: public
title: Predicting Rankings of Software Verification Tools
type: conference
user_id: '15504'
year: '2017'
...
---
_id: '1180'
abstract:
- lang: eng
text: These days, there is a strong rise in the needs for machine learning applications,
requiring an automation of machine learning engineering which is referred to as
AutoML. In AutoML the selection, composition and parametrization of machine learning
algorithms is automated and tailored to a specific problem, resulting in a machine
learning pipeline. Current approaches reduce the AutoML problem to optimization
of hyperparameters. Based on recursive task networks, in this paper we present
one approach from the field of automated planning and one evolutionary optimization
approach. Instead of simply parametrizing a given pipeline, this allows for structure
optimization of machine learning pipelines, as well. We evaluate the two approaches
in an extensive evaluation, finding both approaches to have their strengths in
different areas. Moreover, the two approaches outperform the state-of-the-art
tool Auto-WEKA in many settings.
author:
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Wever MD, Mohr F, Hüllermeier E. Automatic Machine Learning: Hierachical Planning
Versus Evolutionary Optimization. In: 27th Workshop Computational Intelligence.
Dortmund; 2017.'
apa: 'Wever, M. D., Mohr, F., & Hüllermeier, E. (2017). Automatic Machine Learning:
Hierachical Planning Versus Evolutionary Optimization. In 27th Workshop Computational
Intelligence. Dortmund.'
bibtex: '@inproceedings{Wever_Mohr_Hüllermeier_2017, place={Dortmund}, title={Automatic
Machine Learning: Hierachical Planning Versus Evolutionary Optimization}, booktitle={27th
Workshop Computational Intelligence}, author={Wever, Marcel Dominik and Mohr,
Felix and Hüllermeier, Eyke}, year={2017} }'
chicago: 'Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “Automatic Machine
Learning: Hierachical Planning Versus Evolutionary Optimization.” In 27th Workshop
Computational Intelligence. Dortmund, 2017.'
ieee: 'M. D. Wever, F. Mohr, and E. Hüllermeier, “Automatic Machine Learning: Hierachical
Planning Versus Evolutionary Optimization,” in 27th Workshop Computational
Intelligence, Dortmund, 2017.'
mla: 'Wever, Marcel Dominik, et al. “Automatic Machine Learning: Hierachical Planning
Versus Evolutionary Optimization.” 27th Workshop Computational Intelligence,
2017.'
short: 'M.D. Wever, F. Mohr, E. Hüllermeier, in: 27th Workshop Computational Intelligence,
Dortmund, 2017.'
conference:
end_date: 2017-11-24
location: Dortmund
name: 27th Workshop Computational Intelligence
start_date: 2017-11-23
date_created: 2018-02-22T07:19:18Z
date_updated: 2022-01-06T06:51:09Z
ddc:
- '000'
department:
- _id: '355'
file:
- access_level: closed
content_type: application/pdf
creator: wever
date_created: 2018-11-06T15:28:09Z
date_updated: 2018-11-06T15:28:09Z
file_id: '5387'
file_name: CI Workshop AutoML.pdf
file_size: 323589
relation: main_file
success: 1
file_date_updated: 2018-11-06T15:28:09Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://publikationen.bibliothek.kit.edu/1000074341/4643874
oa: '1'
place: Dortmund
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
publication: 27th Workshop Computational Intelligence
publication_status: published
status: public
title: 'Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization'
type: conference
user_id: '49109'
year: '2017'
...
---
_id: '190'
abstract:
- lang: eng
text: Today, software components are provided by global markets in the form of services.
In order to optimally satisfy service requesters and service providers, adequate
techniques for automatic service matching are needed. However, a requester’s requirements
may be vague and the information available about a provided service may be incomplete.
As a consequence, fuzziness is induced into the matching procedure. The contribution
of this paper is the development of a systematic matching procedure that leverages
concepts and techniques from fuzzy logic and possibility theory based on our formal
distinction between different sources and types of fuzziness in the context of
service matching. In contrast to existing methods, our approach is able to deal
with imprecision and incompleteness in service specifications and to inform users
about the extent of induced fuzziness in order to improve the user’s decision-making.
We demonstrate our approach on the example of specifications for service reputation
based on ratings given by previous users. Our evaluation based on real service
ratings shows the utility and applicability of our approach.
author:
- first_name: Marie Christin
full_name: Platenius, Marie Christin
last_name: Platenius
- first_name: Ammar
full_name: Shaker, Ammar
last_name: Shaker
- first_name: Matthias
full_name: Becker, Matthias
last_name: Becker
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Wilhelm
full_name: Schäfer, Wilhelm
last_name: Schäfer
citation:
ama: Platenius MC, Shaker A, Becker M, Hüllermeier E, Schäfer W. Imprecise Matching
of Requirements Specifications for Software Services using Fuzzy Logic. IEEE
Transactions on Software Engineering (TSE), presented at ICSE 2017. 2016;(8):739-759.
doi:10.1109/TSE.2016.2632115
apa: Platenius, M. C., Shaker, A., Becker, M., Hüllermeier, E., & Schäfer, W.
(2016). Imprecise Matching of Requirements Specifications for Software Services
using Fuzzy Logic. IEEE Transactions on Software Engineering (TSE), Presented
at ICSE 2017, (8), 739–759. https://doi.org/10.1109/TSE.2016.2632115
bibtex: '@article{Platenius_Shaker_Becker_Hüllermeier_Schäfer_2016, title={Imprecise
Matching of Requirements Specifications for Software Services using Fuzzy Logic},
DOI={10.1109/TSE.2016.2632115},
number={8}, journal={IEEE Transactions on Software Engineering (TSE), presented
at ICSE 2017}, publisher={IEEE}, author={Platenius, Marie Christin and Shaker,
Ammar and Becker, Matthias and Hüllermeier, Eyke and Schäfer, Wilhelm}, year={2016},
pages={739–759} }'
chicago: 'Platenius, Marie Christin, Ammar Shaker, Matthias Becker, Eyke Hüllermeier,
and Wilhelm Schäfer. “Imprecise Matching of Requirements Specifications for Software
Services Using Fuzzy Logic.” IEEE Transactions on Software Engineering (TSE),
Presented at ICSE 2017, no. 8 (2016): 739–59. https://doi.org/10.1109/TSE.2016.2632115.'
ieee: M. C. Platenius, A. Shaker, M. Becker, E. Hüllermeier, and W. Schäfer, “Imprecise
Matching of Requirements Specifications for Software Services using Fuzzy Logic,”
IEEE Transactions on Software Engineering (TSE), presented at ICSE 2017,
no. 8, pp. 739–759, 2016.
mla: Platenius, Marie Christin, et al. “Imprecise Matching of Requirements Specifications
for Software Services Using Fuzzy Logic.” IEEE Transactions on Software Engineering
(TSE), Presented at ICSE 2017, no. 8, IEEE, 2016, pp. 739–59, doi:10.1109/TSE.2016.2632115.
short: M.C. Platenius, A. Shaker, M. Becker, E. Hüllermeier, W. Schäfer, IEEE Transactions
on Software Engineering (TSE), Presented at ICSE 2017 (2016) 739–759.
date_created: 2017-10-17T12:41:29Z
date_updated: 2022-01-06T06:53:57Z
ddc:
- '040'
department:
- _id: '355'
doi: 10.1109/TSE.2016.2632115
file:
- access_level: closed
content_type: application/pdf
creator: florida
date_created: 2018-03-21T12:30:31Z
date_updated: 2018-03-21T12:30:31Z
file_id: '1529'
file_name: 190-07755807.pdf
file_size: 5225413
relation: main_file
success: 1
file_date_updated: 2018-03-21T12:30:31Z
has_accepted_license: '1'
issue: '8'
language:
- iso: eng
page: 739-759
project:
- _id: '1'
name: SFB 901
- _id: '9'
name: SFB 901 - Subprojekt B1
- _id: '10'
name: SFB 901 - Subprojekt B2
- _id: '11'
name: SFB 901 - Subprojekt B3
- _id: '3'
name: SFB 901 - Project Area B
publication: IEEE Transactions on Software Engineering (TSE), presented at ICSE 2017
publisher: IEEE
status: public
title: Imprecise Matching of Requirements Specifications for Software Services using
Fuzzy Logic
type: journal_article
user_id: '15504'
year: '2016'
...
---
_id: '225'
abstract:
- lang: eng
text: Image Processing is fundamental for any camera-based vision system. In order
to automate the prototyping process of image processing solutions to some extend,
we propose a holistic, adaptive approach that comprises concepts for specification,
composition, recommendation, execution, and rating of image processing functionality.
The fundamental idea is to realize image processing applications according to
Service-oriented Computing design principles. That is, distinct image processing
functionality is encapsulated in terms of stateless services. Services are then
used as building blocks for more complex image processing functionality. To automatically
compose complex image processing functionality, our proposed approach incorporates
a flexible, Artificial Intelligence planning-based forward search algorithm. Decision-making
between alternative composition steps is supported by a learning recommendation
system, which keeps track of valid composition steps by automatically constructing
a composition grammar. In addition, it adapts to solutions of high quality by
means of feedback-based Reinforcement Learning techniques. A concrete use case
serves as proof of concept and demonstrates the feasibility of our holistic, adaptive
approach.
author:
- first_name: Alexander
full_name: Jungmann, Alexander
last_name: Jungmann
- first_name: Bernd
full_name: Kleinjohann, Bernd
last_name: Kleinjohann
citation:
ama: 'Jungmann A, Kleinjohann B. A Holistic and Adaptive Approach for Automated
Prototyping of Image Processing Functionality. In: Proceedings of the 21st
IEEE International Conference on Emerging Technologies and Factory Automation
(ETFA). ; 2016:1--8. doi:10.1109/ETFA.2016.7733522'
apa: Jungmann, A., & Kleinjohann, B. (2016). A Holistic and Adaptive Approach
for Automated Prototyping of Image Processing Functionality. In Proceedings
of the 21st IEEE International Conference on Emerging Technologies and Factory
Automation (ETFA) (pp. 1--8). https://doi.org/10.1109/ETFA.2016.7733522
bibtex: '@inproceedings{Jungmann_Kleinjohann_2016, title={A Holistic and Adaptive
Approach for Automated Prototyping of Image Processing Functionality}, DOI={10.1109/ETFA.2016.7733522},
booktitle={Proceedings of the 21st IEEE International Conference on Emerging Technologies
and Factory Automation (ETFA)}, author={Jungmann, Alexander and Kleinjohann, Bernd},
year={2016}, pages={1--8} }'
chicago: Jungmann, Alexander, and Bernd Kleinjohann. “A Holistic and Adaptive Approach
for Automated Prototyping of Image Processing Functionality.” In Proceedings
of the 21st IEEE International Conference on Emerging Technologies and Factory
Automation (ETFA), 1--8, 2016. https://doi.org/10.1109/ETFA.2016.7733522.
ieee: A. Jungmann and B. Kleinjohann, “A Holistic and Adaptive Approach for Automated
Prototyping of Image Processing Functionality,” in Proceedings of the 21st
IEEE International Conference on Emerging Technologies and Factory Automation
(ETFA), 2016, pp. 1--8.
mla: Jungmann, Alexander, and Bernd Kleinjohann. “A Holistic and Adaptive Approach
for Automated Prototyping of Image Processing Functionality.” Proceedings of
the 21st IEEE International Conference on Emerging Technologies and Factory Automation
(ETFA), 2016, pp. 1--8, doi:10.1109/ETFA.2016.7733522.
short: 'A. Jungmann, B. Kleinjohann, in: Proceedings of the 21st IEEE International
Conference on Emerging Technologies and Factory Automation (ETFA), 2016, pp. 1--8.'
date_created: 2017-10-17T12:41:35Z
date_updated: 2022-01-06T06:55:34Z
ddc:
- '040'
doi: 10.1109/ETFA.2016.7733522
file:
- access_level: closed
content_type: application/pdf
creator: florida
date_created: 2018-03-21T10:34:35Z
date_updated: 2018-03-21T10:34:35Z
file_id: '1508'
file_name: 225-07733522.pdf
file_size: 1323587
relation: main_file
success: 1
file_date_updated: 2018-03-21T10:34:35Z
has_accepted_license: '1'
page: 1--8
project:
- _id: '1'
name: SFB 901
- _id: '10'
name: SFB 901 - Subprojekt B2
- _id: '3'
name: SFB 901 - Project Area B
publication: Proceedings of the 21st IEEE International Conference on Emerging Technologies
and Factory Automation (ETFA)
status: public
title: A Holistic and Adaptive Approach for Automated Prototyping of Image Processing
Functionality
type: conference
user_id: '15504'
year: '2016'
...
---
_id: '218'
abstract:
- lang: eng
text: In the Image Processing domain, automated generation of complex Image Processing
functionality is highly desirable; e.g., for rapid prototyping. Service composition
techniques, in turn, facilitate automated generation of complex functionality
based on building blocks in terms of services. For that reason, we aim for transferring
the Service Composition paradigm into the Image Processing domain. In this paper,
we present our symbolic composition approach that enables us to automatically
generate Image Processing applications. Functionality of Image Processing services
is described by means of a variant of first-order logic, which grounds on domain
knowledge operationalized in terms of ontologies. A Petri-net formalism serves
as basis for modeling data-flow of services and composed services. A planning-based
composition algorithm automatically composes complex data-flow for a required
functionality. A brief evaluation serves as proof of concept.
author:
- first_name: Alexander
full_name: Jungmann, Alexander
last_name: Jungmann
- first_name: Bernd
full_name: Kleinjohann, Bernd
last_name: Kleinjohann
citation:
ama: 'Jungmann A, Kleinjohann B. Automatic Composition of Service-based Image Processing
Applications. In: Proceedings of the 13th IEEE International Conference on
Services Computing (SCC). ; 2016:106--113. doi:10.1109/SCC.2016.21'
apa: Jungmann, A., & Kleinjohann, B. (2016). Automatic Composition of Service-based
Image Processing Applications. In Proceedings of the 13th IEEE International
Conference on Services Computing (SCC) (pp. 106--113). https://doi.org/10.1109/SCC.2016.21
bibtex: '@inproceedings{Jungmann_Kleinjohann_2016, title={Automatic Composition
of Service-based Image Processing Applications}, DOI={10.1109/SCC.2016.21},
booktitle={Proceedings of the 13th IEEE International Conference on Services Computing
(SCC)}, author={Jungmann, Alexander and Kleinjohann, Bernd}, year={2016}, pages={106--113}
}'
chicago: Jungmann, Alexander, and Bernd Kleinjohann. “Automatic Composition of Service-Based
Image Processing Applications.” In Proceedings of the 13th IEEE International
Conference on Services Computing (SCC), 106--113, 2016. https://doi.org/10.1109/SCC.2016.21.
ieee: A. Jungmann and B. Kleinjohann, “Automatic Composition of Service-based Image
Processing Applications,” in Proceedings of the 13th IEEE International Conference
on Services Computing (SCC), 2016, pp. 106--113.
mla: Jungmann, Alexander, and Bernd Kleinjohann. “Automatic Composition of Service-Based
Image Processing Applications.” Proceedings of the 13th IEEE International
Conference on Services Computing (SCC), 2016, pp. 106--113, doi:10.1109/SCC.2016.21.
short: 'A. Jungmann, B. Kleinjohann, in: Proceedings of the 13th IEEE International
Conference on Services Computing (SCC), 2016, pp. 106--113.'
date_created: 2017-10-17T12:41:34Z
date_updated: 2022-01-06T06:55:14Z
ddc:
- '040'
doi: 10.1109/SCC.2016.21
file:
- access_level: closed
content_type: application/pdf
creator: florida
date_created: 2018-03-21T10:38:44Z
date_updated: 2018-03-21T10:38:44Z
file_id: '1515'
file_name: 218-07557442.pdf
file_size: 836658
relation: main_file
success: 1
file_date_updated: 2018-03-21T10:38:44Z
has_accepted_license: '1'
page: 106--113
project:
- _id: '1'
name: SFB 901
- _id: '10'
name: SFB 901 - Subprojekt B2
- _id: '3'
name: SFB 901 - Project Area B
publication: Proceedings of the 13th IEEE International Conference on Services Computing
(SCC)
status: public
title: Automatic Composition of Service-based Image Processing Applications
type: conference
user_id: '15504'
year: '2016'
...
---
_id: '280'
abstract:
- lang: eng
text: The Collaborative Research Centre "On-The-Fly Computing" works on foundations
and principles for the vision of the Future Internet. It proposes the paradigm
of On-The-Fly Computing, which tackles emerging worldwide service markets. In
these markets, service providers trade software, platform, and infrastructure
as a service. Service requesters state requirements on services. To satisfy these
requirements, the new role of brokers, who are (human) actors building service
compositions on the fly, is introduced. Brokers have to specify service compositions
formally and comprehensively using a domain-specific language (DSL), and to use
service matching for the discovery of the constituent services available in the
market. The broker's choice of the DSL and matching approaches influences her
success of building compositions as distinctive properties of different service
markets play a significant role. In this paper, we propose a new approach of engineering
a situation-specific DSL by customizing a comprehensive, modular DSL and its matching
for given service market properties. This enables the broker to create market-specific
composition specifications and to perform market-specific service matching. As
a result, the broker builds service compositions satisfying the requester's requirements
more accurately. We evaluated the presented concepts using case studies in service
markets for tourism and university management.
author:
- first_name: Svetlana
full_name: Arifulina, Svetlana
last_name: Arifulina
- first_name: Marie Christin
full_name: Platenius, Marie Christin
last_name: Platenius
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Gregor
full_name: Engels, Gregor
id: '107'
last_name: Engels
- first_name: Wilhelm
full_name: Schäfer, Wilhelm
last_name: Schäfer
citation:
ama: 'Arifulina S, Platenius MC, Mohr F, Engels G, Schäfer W. Market-Specific Service
Compositions: Specification and Matching. In: Proceedings of the IEEE 11th
World Congress on Services (SERVICES), Visionary Track: Service Composition for
the Future Internet. ; 2015:333--340. doi:10.1109/SERVICES.2015.58'
apa: 'Arifulina, S., Platenius, M. C., Mohr, F., Engels, G., & Schäfer, W. (2015).
Market-Specific Service Compositions: Specification and Matching. In Proceedings
of the IEEE 11th World Congress on Services (SERVICES), Visionary Track: Service
Composition for the Future Internet (pp. 333--340). https://doi.org/10.1109/SERVICES.2015.58'
bibtex: '@inproceedings{Arifulina_Platenius_Mohr_Engels_Schäfer_2015, title={Market-Specific
Service Compositions: Specification and Matching}, DOI={10.1109/SERVICES.2015.58},
booktitle={Proceedings of the IEEE 11th World Congress on Services (SERVICES),
Visionary Track: Service Composition for the Future Internet}, author={Arifulina,
Svetlana and Platenius, Marie Christin and Mohr, Felix and Engels, Gregor and
Schäfer, Wilhelm}, year={2015}, pages={333--340} }'
chicago: 'Arifulina, Svetlana, Marie Christin Platenius, Felix Mohr, Gregor Engels,
and Wilhelm Schäfer. “Market-Specific Service Compositions: Specification and
Matching.” In Proceedings of the IEEE 11th World Congress on Services (SERVICES),
Visionary Track: Service Composition for the Future Internet, 333--340, 2015.
https://doi.org/10.1109/SERVICES.2015.58.'
ieee: 'S. Arifulina, M. C. Platenius, F. Mohr, G. Engels, and W. Schäfer, “Market-Specific
Service Compositions: Specification and Matching,” in Proceedings of the IEEE
11th World Congress on Services (SERVICES), Visionary Track: Service Composition
for the Future Internet, 2015, pp. 333--340.'
mla: 'Arifulina, Svetlana, et al. “Market-Specific Service Compositions: Specification
and Matching.” Proceedings of the IEEE 11th World Congress on Services (SERVICES),
Visionary Track: Service Composition for the Future Internet, 2015, pp. 333--340,
doi:10.1109/SERVICES.2015.58.'
short: 'S. Arifulina, M.C. Platenius, F. Mohr, G. Engels, W. Schäfer, in: Proceedings
of the IEEE 11th World Congress on Services (SERVICES), Visionary Track: Service
Composition for the Future Internet, 2015, pp. 333--340.'
date_created: 2017-10-17T12:41:46Z
date_updated: 2022-01-06T06:57:49Z
ddc:
- '040'
department:
- _id: '66'
- _id: '76'
- _id: '355'
doi: 10.1109/SERVICES.2015.58
file:
- access_level: closed
content_type: application/pdf
creator: florida
date_created: 2018-03-21T09:26:04Z
date_updated: 2018-03-21T09:26:04Z
file_id: '1470'
file_name: 280-07196546.pdf
file_size: 234260
relation: main_file
success: 1
file_date_updated: 2018-03-21T09:26:04Z
has_accepted_license: '1'
language:
- iso: eng
page: 333--340
project:
- _id: '1'
name: SFB 901
- _id: '9'
name: SFB 901 - Subprojekt B1
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '3'
name: SFB 901 - Project Area B
publication: 'Proceedings of the IEEE 11th World Congress on Services (SERVICES),
Visionary Track: Service Composition for the Future Internet'
status: public
title: 'Market-Specific Service Compositions: Specification and Matching'
type: conference
user_id: '477'
year: '2015'
...
---
_id: '245'
abstract:
- lang: eng
text: In cloud computing, software architects develop systems for virtually unlimited
resources that cloud providers account on a pay-per-use basis. Elasticity management
systems provision these resources autonomously to deal with changing workload.
Such changing workloads call for new objective metrics allowing architects to
quantify quality properties like scalability, elasticity, and efficiency, e.g.,
for requirements/SLO engineering and software design analysis. In literature,
initial metrics for these properties have been proposed. However, current metrics
lack a systematic derivation and assume knowledge of implementation details like
resource handling. Therefore, these metrics are inapplicable where such knowledge
is unavailable.To cope with these lacks, this short paper derives metrics for
scalability, elasticity, and efficiency properties of cloud computing systems
using the goal question metric (GQM) method. Our derivation uses a running example
that outlines characteristics of cloud computing systems. Eventually, this example
allows us to set up a systematic GQM plan and to derive an initial set of six
new metrics. We particularly show that our GQM plan allows to classify existing
metrics.
author:
- first_name: Matthias
full_name: Becker, Matthias
last_name: Becker
- first_name: Sebastian
full_name: Lehrig, Sebastian
last_name: Lehrig
- first_name: Steffen
full_name: Becker, Steffen
last_name: Becker
citation:
ama: 'Becker M, Lehrig S, Becker S. Systematically Deriving Quality Metrics for
Cloud Computing Systems. In: Proceedings of the 6th ACM/SPEC International
Conference on Performance Engineering. ICPE ’15. New York, NY, USA; 2015:169--174.
doi:10.1145/2668930.2688043'
apa: Becker, M., Lehrig, S., & Becker, S. (2015). Systematically Deriving Quality
Metrics for Cloud Computing Systems. In Proceedings of the 6th ACM/SPEC International
Conference on Performance Engineering (pp. 169--174). New York, NY, USA. https://doi.org/10.1145/2668930.2688043
bibtex: '@inproceedings{Becker_Lehrig_Becker_2015, place={New York, NY, USA}, series={ICPE
’15}, title={Systematically Deriving Quality Metrics for Cloud Computing Systems},
DOI={10.1145/2668930.2688043},
booktitle={Proceedings of the 6th ACM/SPEC International Conference on Performance
Engineering}, author={Becker, Matthias and Lehrig, Sebastian and Becker, Steffen},
year={2015}, pages={169--174}, collection={ICPE ’15} }'
chicago: Becker, Matthias, Sebastian Lehrig, and Steffen Becker. “Systematically
Deriving Quality Metrics for Cloud Computing Systems.” In Proceedings of the
6th ACM/SPEC International Conference on Performance Engineering, 169--174.
ICPE ’15. New York, NY, USA, 2015. https://doi.org/10.1145/2668930.2688043.
ieee: M. Becker, S. Lehrig, and S. Becker, “Systematically Deriving Quality Metrics
for Cloud Computing Systems,” in Proceedings of the 6th ACM/SPEC International
Conference on Performance Engineering, 2015, pp. 169--174.
mla: Becker, Matthias, et al. “Systematically Deriving Quality Metrics for Cloud
Computing Systems.” Proceedings of the 6th ACM/SPEC International Conference
on Performance Engineering, 2015, pp. 169--174, doi:10.1145/2668930.2688043.
short: 'M. Becker, S. Lehrig, S. Becker, in: Proceedings of the 6th ACM/SPEC International
Conference on Performance Engineering, New York, NY, USA, 2015, pp. 169--174.'
date_created: 2017-10-17T12:41:39Z
date_updated: 2022-01-06T06:56:26Z
ddc:
- '040'
doi: 10.1145/2668930.2688043
file:
- access_level: closed
content_type: application/pdf
creator: florida
date_created: 2018-03-21T09:47:47Z
date_updated: 2018-03-21T09:47:47Z
file_id: '1493'
file_name: 245-paper_02.pdf
file_size: 462675
relation: main_file
success: 1
file_date_updated: 2018-03-21T09:47:47Z
has_accepted_license: '1'
language:
- iso: eng
page: 169--174
place: New York, NY, USA
project:
- _id: '1'
name: SFB 901
- _id: '10'
name: SFB 901 - Subprojekt B2
- _id: '3'
name: SFB 901 - Project Area B
publication: Proceedings of the 6th ACM/SPEC International Conference on Performance
Engineering
series_title: ICPE '15
status: public
title: Systematically Deriving Quality Metrics for Cloud Computing Systems
type: conference
user_id: '477'
year: '2015'
...
---
_id: '323'
abstract:
- lang: eng
text: On-the-fly composition of service-based software solutions is still a challenging
task. Even more challenges emerge when facing automatic service composition in
markets of composed services for end users. In this paper, we focus on the functional
discrepancy between “what a user wants” specified in terms of a request and “what
a user gets” when executing a composed service. To meet the challenge of functional
discrepancy, we propose the combination of existing symbolic composition approaches
with machine learning techniques. We developed a learning recommendation system
that expands the capabilities of existing composition algorithms to facilitate
adaptivity and consequently reduces functional discrepancy. As a representative
of symbolic techniques, an Artificial Intelligence planning based approach produces
solutions that are correct with respect to formal specifications. Our learning
recommendation system supports the symbolic approach in decision-making. Reinforcement
Learning techniques enable the recommendation system to adjust its recommendation
strategy over time based on user ratings. We implemented the proposed functionality
in terms of a prototypical composition framework. Preliminary results from experiments
conducted in the image processing domain illustrate the benefit of combining both
complementary techniques.
author:
- first_name: Alexander
full_name: Jungmann, Alexander
last_name: Jungmann
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
citation:
ama: Jungmann A, Mohr F. An approach towards adaptive service composition in markets
of composed services. Journal of Internet Services and Applications. 2015;(1):1-18.
doi:10.1186/s13174-015-0022-8
apa: Jungmann, A., & Mohr, F. (2015). An approach towards adaptive service composition
in markets of composed services. Journal of Internet Services and Applications,
(1), 1–18. https://doi.org/10.1186/s13174-015-0022-8
bibtex: '@article{Jungmann_Mohr_2015, title={An approach towards adaptive service
composition in markets of composed services}, DOI={10.1186/s13174-015-0022-8},
number={1}, journal={Journal of Internet Services and Applications}, publisher={Springer},
author={Jungmann, Alexander and Mohr, Felix}, year={2015}, pages={1–18} }'
chicago: 'Jungmann, Alexander, and Felix Mohr. “An Approach towards Adaptive Service
Composition in Markets of Composed Services.” Journal of Internet Services
and Applications, no. 1 (2015): 1–18. https://doi.org/10.1186/s13174-015-0022-8.'
ieee: A. Jungmann and F. Mohr, “An approach towards adaptive service composition
in markets of composed services,” Journal of Internet Services and Applications,
no. 1, pp. 1–18, 2015.
mla: Jungmann, Alexander, and Felix Mohr. “An Approach towards Adaptive Service
Composition in Markets of Composed Services.” Journal of Internet Services
and Applications, no. 1, Springer, 2015, pp. 1–18, doi:10.1186/s13174-015-0022-8.
short: A. Jungmann, F. Mohr, Journal of Internet Services and Applications (2015)
1–18.
date_created: 2017-10-17T12:41:55Z
date_updated: 2022-01-06T06:59:06Z
ddc:
- '040'
department:
- _id: '355'
doi: 10.1186/s13174-015-0022-8
file:
- access_level: closed
content_type: application/pdf
creator: florida
date_created: 2018-03-20T07:39:17Z
date_updated: 2018-03-20T07:39:17Z
file_id: '1429'
file_name: 323-An_approach_towards_adaptive_service_composition_in_markets_of_composed_services.pdf
file_size: 2842281
relation: main_file
success: 1
file_date_updated: 2018-03-20T07:39:17Z
has_accepted_license: '1'
issue: '1'
language:
- iso: eng
page: 1-18
project:
- _id: '1'
name: SFB 901
- _id: '10'
name: SFB 901 - Subprojekt B2
- _id: '3'
name: SFB 901 - Project Area B
publication: Journal of Internet Services and Applications
publisher: Springer
status: public
title: An approach towards adaptive service composition in markets of composed services
type: journal_article
user_id: '477'
year: '2015'
...
---
_id: '324'
abstract:
- lang: eng
text: Services are self-contained software components that can beused platform independent
and that aim at maximizing software reuse. Abasic concern in service oriented
architectures is to measure the reusabilityof services. One of the most important
qualities is the functionalreusability, which indicates how relevant the task
is that a service solves.Current metrics for functional reusability of software,
however, have verylittle explanatory power and do not accomplish this goal.This
paper presents a new approach to estimate the functional reusabilityof services
based on their relevance. To this end, it denes the degreeto which a service enables
the execution of other services as its contri-bution. Based on the contribution,
relevance of services is dened as anestimation for their functional reusability.
Explanatory power is obtainedby normalizing relevance values with a reference
service. The applicationof the metric to a service test set conrms its supposed
capabilities.
author:
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
citation:
ama: 'Mohr F. A Metric for Functional Reusability of Services. In: Proceedings
of the 14th International Conference on Software Reuse (ICSR). LNCS. ; 2015:298--313.
doi:10.1007/978-3-319-14130-5_21'
apa: Mohr, F. (2015). A Metric for Functional Reusability of Services. In Proceedings
of the 14th International Conference on Software Reuse (ICSR) (pp. 298--313).
https://doi.org/10.1007/978-3-319-14130-5_21
bibtex: '@inproceedings{Mohr_2015, series={LNCS}, title={A Metric for Functional
Reusability of Services}, DOI={10.1007/978-3-319-14130-5_21},
booktitle={Proceedings of the 14th International Conference on Software Reuse
(ICSR)}, author={Mohr, Felix}, year={2015}, pages={298--313}, collection={LNCS}
}'
chicago: Mohr, Felix. “A Metric for Functional Reusability of Services.” In Proceedings
of the 14th International Conference on Software Reuse (ICSR), 298--313. LNCS,
2015. https://doi.org/10.1007/978-3-319-14130-5_21.
ieee: F. Mohr, “A Metric for Functional Reusability of Services,” in Proceedings
of the 14th International Conference on Software Reuse (ICSR), 2015, pp. 298--313.
mla: Mohr, Felix. “A Metric for Functional Reusability of Services.” Proceedings
of the 14th International Conference on Software Reuse (ICSR), 2015, pp. 298--313,
doi:10.1007/978-3-319-14130-5_21.
short: 'F. Mohr, in: Proceedings of the 14th International Conference on Software
Reuse (ICSR), 2015, pp. 298--313.'
date_created: 2017-10-17T12:41:55Z
date_updated: 2022-01-06T06:59:07Z
ddc:
- '040'
department:
- _id: '355'
doi: 10.1007/978-3-319-14130-5_21
file:
- access_level: closed
content_type: application/pdf
creator: florida
date_created: 2018-03-20T07:38:44Z
date_updated: 2018-03-20T07:38:44Z
file_id: '1428'
file_name: 324-ICSR-Mohr-15.pdf
file_size: 569475
relation: main_file
success: 1
file_date_updated: 2018-03-20T07:38:44Z
has_accepted_license: '1'
language:
- iso: eng
page: 298--313
project:
- _id: '1'
name: SFB 901
- _id: '10'
name: SFB 901 - Subprojekt B2
- _id: '3'
name: SFB 901 - Project Area B
publication: Proceedings of the 14th International Conference on Software Reuse (ICSR)
series_title: LNCS
status: public
title: A Metric for Functional Reusability of Services
type: conference
user_id: '477'
year: '2015'
...
---
_id: '3343'
abstract:
- lang: eng
text: In this paper we consider an extended variant of query learning where the
hidden concept is embedded in some Boolean circuit. This additional processing
layer modifies query arguments and answers by fixed transformation functions which
are known to the learner. For this scenario, we provide a characterization of
the solution space and an ordering on it. We give a compact representation of
the minimal and maximal solutions as quantified Boolean formulas and we adapt
the original algorithms for exact learning of specific classes of propositional
formulas.
author:
- first_name: Uwe
full_name: Bubeck, Uwe
last_name: Bubeck
- first_name: Hans
full_name: Kleine Büning, Hans
last_name: Kleine Büning
citation:
ama: Bubeck U, Kleine Büning H. Learning Boolean Specifications. Artificial Intelligence.
2015:246-257. doi:10.1016/j.artint.2015.09.003
apa: Bubeck, U., & Kleine Büning, H. (2015). Learning Boolean Specifications.
Artificial Intelligence, 246–257. https://doi.org/10.1016/j.artint.2015.09.003
bibtex: '@article{Bubeck_Kleine Büning_2015, title={Learning Boolean Specifications},
DOI={10.1016/j.artint.2015.09.003},
journal={Artificial Intelligence}, publisher={Elsevier}, author={Bubeck, Uwe and
Kleine Büning, Hans}, year={2015}, pages={246–257} }'
chicago: Bubeck, Uwe, and Hans Kleine Büning. “Learning Boolean Specifications.”
Artificial Intelligence, 2015, 246–57. https://doi.org/10.1016/j.artint.2015.09.003.
ieee: U. Bubeck and H. Kleine Büning, “Learning Boolean Specifications,” Artificial
Intelligence, pp. 246–257, 2015.
mla: Bubeck, Uwe, and Hans Kleine Büning. “Learning Boolean Specifications.” Artificial
Intelligence, Elsevier, 2015, pp. 246–57, doi:10.1016/j.artint.2015.09.003.
short: U. Bubeck, H. Kleine Büning, Artificial Intelligence (2015) 246–257.
date_created: 2018-06-25T10:43:19Z
date_updated: 2022-01-06T06:59:10Z
ddc:
- '000'
department:
- _id: '34'
doi: 10.1016/j.artint.2015.09.003
keyword:
- Query learning
- Propositional logic
language:
- iso: eng
page: 246 - 257
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
publication: Artificial Intelligence
publication_identifier:
issn:
- 0004-3702
publisher: Elsevier
status: public
title: Learning Boolean Specifications
type: journal_article
user_id: '315'
year: '2015'
...
---
_id: '315'
abstract:
- lang: eng
text: In this paper, we introduce an approach for combining embedded systems with
Service-oriented Computing techniques based on a concrete application scenario
from the robotics domain. Our proposed Service-oriented Architecture allows for
incorporating computational expensive functionality as services into a distributed
computing environment. Furthermore, our framework facilitates a seamless integration
of embedded systems such as robots as service providers into the computing environment.
The entire communication is based on so-called recipes, which can be interpreted
as autonomous messages that contain all necessary information for executing compositions
of services.
author:
- first_name: Alexander
full_name: Jungmann, Alexander
last_name: Jungmann
- first_name: Jan
full_name: Jatzkowski, Jan
last_name: Jatzkowski
- first_name: Bernd
full_name: Kleinjohann, Bernd
last_name: Kleinjohann
citation:
ama: 'Jungmann A, Jatzkowski J, Kleinjohann B. Combining Service-oriented Computing
with Embedded Systems - A Robotics Case Study. In: Proceedings of the 5th IFIP
International Embedded Systems Symposium. ; 2015.'
apa: Jungmann, A., Jatzkowski, J., & Kleinjohann, B. (2015). Combining Service-oriented
Computing with Embedded Systems - A Robotics Case Study. In Proceedings of
the 5th IFIP International Embedded Systems Symposium.
bibtex: '@inproceedings{Jungmann_Jatzkowski_Kleinjohann_2015, title={Combining Service-oriented
Computing with Embedded Systems - A Robotics Case Study}, booktitle={Proceedings
of the 5th IFIP International Embedded Systems Symposium}, author={Jungmann, Alexander
and Jatzkowski, Jan and Kleinjohann, Bernd}, year={2015} }'
chicago: Jungmann, Alexander, Jan Jatzkowski, and Bernd Kleinjohann. “Combining
Service-Oriented Computing with Embedded Systems - A Robotics Case Study.” In
Proceedings of the 5th IFIP International Embedded Systems Symposium, 2015.
ieee: A. Jungmann, J. Jatzkowski, and B. Kleinjohann, “Combining Service-oriented
Computing with Embedded Systems - A Robotics Case Study,” in Proceedings of
the 5th IFIP International Embedded Systems Symposium, 2015.
mla: Jungmann, Alexander, et al. “Combining Service-Oriented Computing with Embedded
Systems - A Robotics Case Study.” Proceedings of the 5th IFIP International
Embedded Systems Symposium, 2015.
short: 'A. Jungmann, J. Jatzkowski, B. Kleinjohann, in: Proceedings of the 5th IFIP
International Embedded Systems Symposium, 2015.'
date_created: 2017-10-17T12:41:53Z
date_updated: 2022-01-06T06:58:58Z
ddc:
- '040'
file:
- access_level: closed
content_type: application/pdf
creator: florida
date_created: 2018-03-20T07:43:26Z
date_updated: 2018-03-20T07:43:26Z
file_id: '1436'
file_name: 315-JungmannJatzkowskiKleinjohann.pdf
file_size: 1482481
relation: main_file
success: 1
file_date_updated: 2018-03-20T07:43:26Z
has_accepted_license: '1'
project:
- _id: '1'
name: SFB 901
- _id: '10'
name: SFB 901 - Subprojekt B2
- _id: '3'
name: SFB 901 - Project Area B
publication: Proceedings of the 5th IFIP International Embedded Systems Symposium
status: public
title: Combining Service-oriented Computing with Embedded Systems - A Robotics Case
Study
type: conference
user_id: '15504'
year: '2015'
...