---
_id: '57580'
abstract:
- lang: eng
text: We investigate dispersive and Strichartz estimates for the Schrödinger equation
involving the fractional Laplacian in real hyperbolic spaces and their discrete
analogues, homogeneous trees. Due to the Knapp phenomenon, the Strichartz estimates
on Euclidean spaces for the fractional Laplacian exhibit loss of derivatives.
A similar phenomenon appears on real hyperbolic spaces. However, such a loss disappears
on homogeneous trees, due to the triviality of the estimates for small times.
author:
- first_name: Guendalina
full_name: Palmirotta, Guendalina
id: '109467'
last_name: Palmirotta
- first_name: Yannick
full_name: Sire, Yannick
last_name: Sire
- first_name: Jean-Philippe
full_name: Anker, Jean-Philippe
last_name: Anker
citation:
ama: Palmirotta G, Sire Y, Anker J-P. The Schrödinger equation with fractional Laplacian
on hyperbolic spaces and homogeneous trees. Journal of Differential Equations.
Published online 2026. doi:10.1016/j.jde.2025.114065
apa: Palmirotta, G., Sire, Y., & Anker, J.-P. (2026). The Schrödinger equation
with fractional Laplacian on hyperbolic spaces and homogeneous trees. Journal
of Differential Equations. https://doi.org/10.1016/j.jde.2025.114065
bibtex: '@article{Palmirotta_Sire_Anker_2026, title={The Schrödinger equation with
fractional Laplacian on hyperbolic spaces and homogeneous trees}, DOI={10.1016/j.jde.2025.114065},
journal={Journal of Differential Equations}, publisher={Elsevier}, author={Palmirotta,
Guendalina and Sire, Yannick and Anker, Jean-Philippe}, year={2026} }'
chicago: Palmirotta, Guendalina, Yannick Sire, and Jean-Philippe Anker. “The Schrödinger
Equation with Fractional Laplacian on Hyperbolic Spaces and Homogeneous Trees.”
Journal of Differential Equations, 2026. https://doi.org/10.1016/j.jde.2025.114065.
ieee: 'G. Palmirotta, Y. Sire, and J.-P. Anker, “The Schrödinger equation with fractional
Laplacian on hyperbolic spaces and homogeneous trees,” Journal of Differential
Equations, 2026, doi: 10.1016/j.jde.2025.114065.'
mla: Palmirotta, Guendalina, et al. “The Schrödinger Equation with Fractional Laplacian
on Hyperbolic Spaces and Homogeneous Trees.” Journal of Differential Equations,
Elsevier, 2026, doi:10.1016/j.jde.2025.114065.
short: G. Palmirotta, Y. Sire, J.-P. Anker, Journal of Differential Equations (2026).
date_created: 2024-12-04T16:21:38Z
date_updated: 2026-03-30T12:03:37Z
department:
- _id: '10'
- _id: '548'
doi: 10.1016/j.jde.2025.114065
external_id:
arxiv:
- '2412.00780'
keyword:
- Schrödinger equation
- Fractional Laplacian
- Dispersive estimates
- Strichartz estimates
- Real hyperbolic spaces
- Homogeneous trees
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1016/j.jde.2025.114065
oa: '1'
project:
- _id: '356'
name: 'TRR 358 - B02: TRR 358 - Spektraltheorie in höherem Rang und unendlichem
Volumen (Teilprojekt B02)'
publication: Journal of Differential Equations
publication_status: published
publisher: Elsevier
related_material:
link:
- relation: confirmation
url: https://www.sciencedirect.com/science/article/pii/S0022039625010927?via%3Dihub
status: public
title: The Schrödinger equation with fractional Laplacian on hyperbolic spaces and
homogeneous trees
type: journal_article
user_id: '109467'
year: '2026'
...
---
_id: '60680'
abstract:
- lang: eng
text: "Classical machine learning techniques often struggle with overfitting and
unreliable predictions when exposed to novel conditions. Introducing causality
into the modelling process offers a promising way to mitigate these challenges
by enhancing predictive robustness. However, constructing an initial causal graph
manually using domain knowledge is time-consuming, particularly in complex time
series with numerous variables. To address this, causal discovery algorithms can
provide a preliminary causal structure that domain experts can refine. This study
investigates causal feature selection with domain knowledge using a data center
system as an example. We use simulated time-series data to compare \r\ndifferent
causal feature selection with traditional machine-learning feature selection methods.
Our results show that predictions based on causal features are more robust compared
to those derived from traditional methods. These findings underscore the potential
of combining causal discovery algorithms with human expertise to improve machine
learning applications."
author:
- first_name: David Ricardo
full_name: Zapata Gonzalez, David Ricardo
id: '105506'
last_name: Zapata Gonzalez
- first_name: Marcel
full_name: Meyer, Marcel
id: '105120'
last_name: Meyer
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Zapata Gonzalez DR, Meyer M, Müller O. Bridging the gap between data-driven
and theory-driven modelling – leveraging causal machine learning for integrative
modelling of dynamical systems. In: ; 2025.'
apa: Zapata Gonzalez, D. R., Meyer, M., & Müller, O. (2025). Bridging the
gap between data-driven and theory-driven modelling – leveraging causal machine
learning for integrative modelling of dynamical systems. European Conference
on Information Systems, Amman, Jordan.
bibtex: '@inproceedings{Zapata Gonzalez_Meyer_Müller_2025, title={Bridging the gap
between data-driven and theory-driven modelling – leveraging causal machine learning
for integrative modelling of dynamical systems}, author={Zapata Gonzalez, David
Ricardo and Meyer, Marcel and Müller, Oliver}, year={2025} }'
chicago: Zapata Gonzalez, David Ricardo, Marcel Meyer, and Oliver Müller. “Bridging
the Gap between Data-Driven and Theory-Driven Modelling – Leveraging Causal Machine
Learning for Integrative Modelling of Dynamical Systems,” 2025.
ieee: D. R. Zapata Gonzalez, M. Meyer, and O. Müller, “Bridging the gap between
data-driven and theory-driven modelling – leveraging causal machine learning for
integrative modelling of dynamical systems,” presented at the European Conference
on Information Systems, Amman, Jordan, 2025.
mla: Zapata Gonzalez, David Ricardo, et al. Bridging the Gap between Data-Driven
and Theory-Driven Modelling – Leveraging Causal Machine Learning for Integrative
Modelling of Dynamical Systems. 2025.
short: 'D.R. Zapata Gonzalez, M. Meyer, O. Müller, in: 2025.'
conference:
end_date: 18.06.2025
location: Amman, Jordan
name: European Conference on Information Systems
start_date: 16.06.2025
date_created: 2025-07-21T07:52:03Z
date_updated: 2025-07-22T06:30:37Z
department:
- _id: '196'
keyword:
- Causal Machine Learning
- Causality in Time Series
- Causal Discovery
- Human-Machine Collaboration
language:
- iso: eng
main_file_link:
- url: https://aisel.aisnet.org/ecis2025/bus_analytics/bus_analytics/2/
status: public
title: Bridging the gap between data-driven and theory-driven modelling – leveraging
causal machine learning for integrative modelling of dynamical systems
type: conference
user_id: '72849'
year: '2025'
...
---
_id: '49109'
abstract:
- lang: eng
text: "We propose a diarization system, that estimates “who spoke when” based on
spatial information, to be used as a front-end of a meeting transcription system
running on the signals gathered from an acoustic sensor network (ASN). Although
the\r\nspatial distribution of the microphones is advantageous, exploiting the
spatial diversity for diarization and signal enhancement is challenging, because
the microphones’ positions are typically unknown, and the recorded signals are
initially unsynchronized in general. Here, we approach these issues by first blindly
synchronizing the signals and then estimating time differences of arrival (TDOAs).
The TDOA information is exploited to estimate the speakers’ activity, even in
the presence of multiple speakers being simultaneously active. This speaker activity
information serves as a guide for a spatial mixture model, on which basis the
individual speaker’s signals are extracted via beamforming. Finally, the extracted
signals are forwarded to a speech recognizer. Additionally, a novel initialization
scheme for spatial mixture models based on the TDOA estimates is proposed. Experiments
conducted on real recordings from the LibriWASN data set have shown that our proposed
system is advantageous compared to a system using a spatial mixture model, which
does not make use\r\nof external diarization information."
author:
- first_name: Tobias
full_name: Gburrek, Tobias
id: '44006'
last_name: Gburrek
- first_name: Joerg
full_name: Schmalenstroeer, Joerg
id: '460'
last_name: Schmalenstroeer
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
citation:
ama: 'Gburrek T, Schmalenstroeer J, Haeb-Umbach R. Spatial Diarization for Meeting
Transcription with Ad-Hoc Acoustic Sensor Networks. In: Proc. Asilomar Conference
on Signals, Systems, and Computers. ; 2023.'
apa: Gburrek, T., Schmalenstroeer, J., & Haeb-Umbach, R. (2023). Spatial Diarization
for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks. Proc. Asilomar
Conference on Signals, Systems, and Computers. 57th Asilomar Conference on
Signals, Systems, and Computers.
bibtex: '@inproceedings{Gburrek_Schmalenstroeer_Haeb-Umbach_2023, title={Spatial
Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks}, booktitle={Proc.
Asilomar Conference on Signals, Systems, and Computers}, author={Gburrek, Tobias
and Schmalenstroeer, Joerg and Haeb-Umbach, Reinhold}, year={2023} }'
chicago: Gburrek, Tobias, Joerg Schmalenstroeer, and Reinhold Haeb-Umbach. “Spatial
Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks.” In
Proc. Asilomar Conference on Signals, Systems, and Computers, 2023.
ieee: T. Gburrek, J. Schmalenstroeer, and R. Haeb-Umbach, “Spatial Diarization for
Meeting Transcription with Ad-Hoc Acoustic Sensor Networks,” presented at the
57th Asilomar Conference on Signals, Systems, and Computers, 2023.
mla: Gburrek, Tobias, et al. “Spatial Diarization for Meeting Transcription with
Ad-Hoc Acoustic Sensor Networks.” Proc. Asilomar Conference on Signals, Systems,
and Computers, 2023.
short: 'T. Gburrek, J. Schmalenstroeer, R. Haeb-Umbach, in: Proc. Asilomar Conference
on Signals, Systems, and Computers, 2023.'
conference:
end_date: 2023-11-01
name: 57th Asilomar Conference on Signals, Systems, and Computers
start_date: 2023-10-31
date_created: 2023-11-22T07:52:29Z
date_updated: 2023-11-22T07:58:49Z
ddc:
- '004'
department:
- _id: '54'
file:
- access_level: open_access
content_type: application/pdf
creator: schmalen
date_created: 2023-11-22T07:51:18Z
date_updated: 2023-11-22T07:58:49Z
file_id: '49110'
file_name: asilomar.pdf
file_size: 212317
relation: main_file
file_date_updated: 2023-11-22T07:58:49Z
has_accepted_license: '1'
keyword:
- Diarization
- time difference of arrival
- ad-hoc acoustic sensor network
- meeting transcription
language:
- iso: eng
oa: '1'
publication: Proc. Asilomar Conference on Signals, Systems, and Computers
quality_controlled: '1'
status: public
title: Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor Networks
type: conference
user_id: '460'
year: '2023'
...
---
_id: '37312'
abstract:
- lang: eng
text: Optimal decision making requires appropriate evaluation of advice. Recent
literature reports that algorithm aversion reduces the effectiveness of predictive
algorithms. However, it remains unclear how people recover from bad advice given
by an otherwise good advisor. Previous work has focused on algorithm aversion
at a single time point. We extend this work by examining successive decisions
in a time series forecasting task using an online between-subjects experiment
(N = 87). Our empirical results do not confirm algorithm aversion immediately
after bad advice. The estimated effect suggests an increasing algorithm appreciation
over time. Our work extends the current knowledge on algorithm aversion with insights
into how weight on advice is adjusted over consecutive tasks. Since most forecasting
tasks are not one-off decisions, this also has implications for practitioners.
author:
- first_name: Dirk
full_name: Leffrang, Dirk
id: '51271'
last_name: Leffrang
orcid: 0000-0001-9004-2391
- first_name: Kevin
full_name: Bösch, Kevin
last_name: Bösch
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Leffrang D, Bösch K, Müller O. Do People Recover from Algorithm Aversion?
An Experimental Study of Algorithm Aversion over Time. In: Hawaii International
Conference on System Sciences. ; 2023.'
apa: Leffrang, D., Bösch, K., & Müller, O. (2023). Do People Recover from Algorithm
Aversion? An Experimental Study of Algorithm Aversion over Time. Hawaii International
Conference on System Sciences. Hawaii International Conference on System Sciences.
bibtex: '@inproceedings{Leffrang_Bösch_Müller_2023, title={Do People Recover from
Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time}, booktitle={Hawaii
International Conference on System Sciences}, author={Leffrang, Dirk and Bösch,
Kevin and Müller, Oliver}, year={2023} }'
chicago: Leffrang, Dirk, Kevin Bösch, and Oliver Müller. “Do People Recover from
Algorithm Aversion? An Experimental Study of Algorithm Aversion over Time.” In
Hawaii International Conference on System Sciences, 2023.
ieee: D. Leffrang, K. Bösch, and O. Müller, “Do People Recover from Algorithm Aversion?
An Experimental Study of Algorithm Aversion over Time,” presented at the Hawaii
International Conference on System Sciences, 2023.
mla: Leffrang, Dirk, et al. “Do People Recover from Algorithm Aversion? An Experimental
Study of Algorithm Aversion over Time.” Hawaii International Conference on
System Sciences, 2023.
short: 'D. Leffrang, K. Bösch, O. Müller, in: Hawaii International Conference on
System Sciences, 2023.'
conference:
name: Hawaii International Conference on System Sciences
date_created: 2023-01-18T10:53:51Z
date_updated: 2024-01-10T09:52:59Z
department:
- _id: '196'
keyword:
- Algorithm aversion
- Time series
- Decision making
- Advice taking
- Forecasting
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://scholarspace.manoa.hawaii.edu/items/62b58ddc-895c-48c3-8194-522a1758a26f
oa: '1'
publication: Hawaii International Conference on System Sciences
status: public
title: Do People Recover from Algorithm Aversion? An Experimental Study of Algorithm
Aversion over Time
type: conference
user_id: '51271'
year: '2023'
...
---
_id: '50479'
abstract:
- lang: eng
text: Verifying assertions is an essential part of creating and maintaining knowledge
graphs. Most often, this task cannot be carried out manually due to the sheer
size of modern knowledge graphs. Hence, automatic fact-checking approaches have
been proposed over the last decade. These approaches aim to compute automatically
whether a given assertion is correct or incorrect. However, most fact-checking
approaches are binary classifiers that fail to consider the volatility of some
assertions, i.e., the fact that such assertions are only valid at certain times
or for specific time intervals. Moreover, the few approaches able to predict when
an assertion was valid (i.e., time-point prediction approaches) rely on manual
feature engineering. This paper presents TEMPORALFC, a temporal fact-checking
approach that uses multiple sources of background knowledge to assess the veracity
and temporal validity of a given assertion. We evaluate TEMPORALFC on two datasets
and compare it to the state of the art in fact-checking and time-point prediction.
Our results suggest that TEMPORALFC outperforms the state of the art on the fact-checking
task by 0.13 to 0.15 in terms of Area Under the Receiver Operating Characteristic
curve and on the time-point prediction task by 0.25 to 0.27 in terms of Mean Reciprocal
Rank. Our code is open-source and can be found at https://github.com/dice-group/TemporalFC.
author:
- first_name: Umair
full_name: Qudus, Umair
last_name: Qudus
- first_name: Michael
full_name: Röder, Michael
last_name: Röder
- first_name: Sabrina
full_name: Kirrane, Sabrina
last_name: Kirrane
- first_name: Axel-Cyrille Ngonga
full_name: Ngomo, Axel-Cyrille Ngonga
last_name: Ngomo
citation:
ama: 'Qudus U, Röder M, Kirrane S, Ngomo A-CN. TemporalFC: A Temporal Fact Checking
Approach over Knowledge Graphs. In: R. Payne T, Presutti V, Qi G, et al., eds.
The Semantic Web – ISWC 2023. Vol 14265. Lecture Notes in Computer Science.
Springer, Cham; 2023:465–483. doi:10.1007/978-3-031-47240-4_25'
apa: 'Qudus, U., Röder, M., Kirrane, S., & Ngomo, A.-C. N. (2023). TemporalFC:
A Temporal Fact Checking Approach over Knowledge Graphs. In T. R. Payne, V. Presutti,
G. Qi, M. Poveda-Villalón, G. Stoilos, L. Hollink, Z. Kaoudi, G. Cheng, &
J. Li (Eds.), The Semantic Web – ISWC 2023 (Vol. 14265, pp. 465–483). Springer,
Cham. https://doi.org/10.1007/978-3-031-47240-4_25'
bibtex: '@inproceedings{Qudus_Röder_Kirrane_Ngomo_2023, place={Cham}, series={ Lecture
Notes in Computer Science}, title={TemporalFC: A Temporal Fact Checking Approach
over Knowledge Graphs}, volume={14265}, DOI={10.1007/978-3-031-47240-4_25},
booktitle={The Semantic Web – ISWC 2023}, publisher={Springer, Cham}, author={Qudus,
Umair and Röder, Michael and Kirrane, Sabrina and Ngomo, Axel-Cyrille Ngonga},
editor={R. Payne, Terry and Presutti, Valentina and Qi, Guilin and Poveda-Villalón,
María and Stoilos, Giorgos and Hollink, Laura and Kaoudi, Zoi and Cheng, Gong
and Li, Juanzi}, year={2023}, pages={465–483}, collection={ Lecture Notes in Computer
Science} }'
chicago: 'Qudus, Umair, Michael Röder, Sabrina Kirrane, and Axel-Cyrille Ngonga
Ngomo. “TemporalFC: A Temporal Fact Checking Approach over Knowledge Graphs.”
In The Semantic Web – ISWC 2023, edited by Terry R. Payne, Valentina Presutti,
Guilin Qi, María Poveda-Villalón, Giorgos Stoilos, Laura Hollink, Zoi Kaoudi,
Gong Cheng, and Juanzi Li, 14265:465–483. Lecture Notes in Computer Science.
Cham: Springer, Cham, 2023. https://doi.org/10.1007/978-3-031-47240-4_25.'
ieee: 'U. Qudus, M. Röder, S. Kirrane, and A.-C. N. Ngomo, “TemporalFC: A Temporal
Fact Checking Approach over Knowledge Graphs,” in The Semantic Web – ISWC 2023,
Athens, Greece, 2023, vol. 14265, pp. 465–483, doi: 10.1007/978-3-031-47240-4_25.'
mla: 'Qudus, Umair, et al. “TemporalFC: A Temporal Fact Checking Approach over Knowledge
Graphs.” The Semantic Web – ISWC 2023, edited by Terry R. Payne et al.,
vol. 14265, Springer, Cham, 2023, pp. 465–483, doi:10.1007/978-3-031-47240-4_25.'
short: 'U. Qudus, M. Röder, S. Kirrane, A.-C.N. Ngomo, in: T. R. Payne, V. Presutti,
G. Qi, M. Poveda-Villalón, G. Stoilos, L. Hollink, Z. Kaoudi, G. Cheng, J. Li
(Eds.), The Semantic Web – ISWC 2023, Springer, Cham, Cham, 2023, pp. 465–483.'
conference:
end_date: 2023-11-10
location: Athens, Greece
name: The Semantic Web – ISWC 2023
start_date: 2023-11-06
date_created: 2024-01-13T11:22:15Z
date_updated: 2024-01-13T11:48:28Z
ddc:
- '006'
department:
- _id: '34'
doi: 10.1007/978-3-031-47240-4_25
editor:
- first_name: Terry
full_name: R. Payne, Terry
last_name: R. Payne
- first_name: Valentina
full_name: Presutti, Valentina
last_name: Presutti
- first_name: Guilin
full_name: Qi, Guilin
last_name: Qi
- first_name: María
full_name: Poveda-Villalón, María
last_name: Poveda-Villalón
- first_name: Giorgos
full_name: Stoilos, Giorgos
last_name: Stoilos
- first_name: Laura
full_name: Hollink, Laura
last_name: Hollink
- first_name: Zoi
full_name: Kaoudi, Zoi
last_name: Kaoudi
- first_name: Gong
full_name: Cheng, Gong
last_name: Cheng
- first_name: Juanzi
full_name: Li, Juanzi
last_name: Li
file:
- access_level: closed
content_type: application/pdf
creator: uqudus
date_created: 2024-01-13T11:25:48Z
date_updated: 2024-01-13T11:25:48Z
file_id: '50480'
file_name: ISWC 2023 TemporalFC-A Temporal Fact Checking approach over Knowledge
Graphs.pdf
file_size: 1944818
relation: main_file
success: 1
file_date_updated: 2024-01-13T11:25:48Z
has_accepted_license: '1'
intvolume: ' 14265'
jel:
- C
keyword:
- temporal fact checking · ensemble learning · transfer learning · time-point prediction
· temporal knowledge graphs
language:
- iso: eng
page: 465–483
place: Cham
project:
- _id: '410'
grant_number: '860801'
name: 'KnowGraphs: KnowGraphs: Knowledge Graphs at Scale'
publication: The Semantic Web – ISWC 2023
publication_identifier:
isbn:
- '9783031472398'
- '9783031472404'
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer, Cham
series_title: ' Lecture Notes in Computer Science'
status: public
title: 'TemporalFC: A Temporal Fact Checking Approach over Knowledge Graphs'
type: conference
user_id: '83392'
volume: 14265
year: '2023'
...
---
_id: '45299'
abstract:
- lang: eng
text: Many applications are driven by Machine Learning (ML) today. While complex
ML models lead to an accurate prediction, their inner decision-making is obfuscated.
However, especially for high-stakes decisions, interpretability and explainability
of the model are necessary. Therefore, we develop a holistic interpretability
and explainability framework (HIEF) to objectively describe and evaluate an intelligent
system’s explainable AI (XAI) capacities. This guides data scientists to create
more transparent models. To evaluate our framework, we analyse 50 real estate
appraisal papers to ensure the robustness of HIEF. Additionally, we identify six
typical types of intelligent systems, so-called archetypes, which range from explanatory
to predictive, and demonstrate how researchers can use the framework to identify
blind-spot topics in their domain. Finally, regarding comprehensiveness, we used
a random sample of six intelligent systems and conducted an applicability check
to provide external validity.
author:
- first_name: Jan-Peter
full_name: Kucklick, Jan-Peter
id: '77066'
last_name: Kucklick
citation:
ama: 'Kucklick J-P. HIEF: a holistic interpretability and explainability framework.
Journal of Decision Systems. Published online 2023:1-41. doi:10.1080/12460125.2023.2207268'
apa: 'Kucklick, J.-P. (2023). HIEF: a holistic interpretability and explainability
framework. Journal of Decision Systems, 1–41. https://doi.org/10.1080/12460125.2023.2207268'
bibtex: '@article{Kucklick_2023, title={HIEF: a holistic interpretability and explainability
framework}, DOI={10.1080/12460125.2023.2207268},
journal={Journal of Decision Systems}, publisher={Taylor & Francis}, author={Kucklick,
Jan-Peter}, year={2023}, pages={1–41} }'
chicago: 'Kucklick, Jan-Peter. “HIEF: A Holistic Interpretability and Explainability
Framework.” Journal of Decision Systems, 2023, 1–41. https://doi.org/10.1080/12460125.2023.2207268.'
ieee: 'J.-P. Kucklick, “HIEF: a holistic interpretability and explainability framework,”
Journal of Decision Systems, pp. 1–41, 2023, doi: 10.1080/12460125.2023.2207268.'
mla: 'Kucklick, Jan-Peter. “HIEF: A Holistic Interpretability and Explainability
Framework.” Journal of Decision Systems, Taylor & Francis, 2023, pp.
1–41, doi:10.1080/12460125.2023.2207268.'
short: J.-P. Kucklick, Journal of Decision Systems (2023) 1–41.
date_created: 2023-05-26T05:04:45Z
date_updated: 2023-05-26T05:08:36Z
department:
- _id: '195'
- _id: '196'
doi: 10.1080/12460125.2023.2207268
keyword:
- Explainable AI (XAI)
- machine learning
- interpretability
- real estate appraisal
- framework
- taxonomy
language:
- iso: eng
main_file_link:
- url: https://www.tandfonline.com/doi/full/10.1080/12460125.2023.2207268
page: 1-41
publication: Journal of Decision Systems
publication_identifier:
issn:
- 1246-0125
- 2116-7052
publication_status: published
publisher: Taylor & Francis
status: public
title: 'HIEF: a holistic interpretability and explainability framework'
type: journal_article
user_id: '77066'
year: '2023'
...
---
_id: '27506'
abstract:
- lang: eng
text: Explainability for machine learning gets more and more important in high-stakes
decisions like real estate appraisal. While traditional hedonic house pricing
models are fed with hard information based on housing attributes, recently also
soft information has been incorporated to increase the predictive performance.
This soft information can be extracted from image data by complex models like
Convolutional Neural Networks (CNNs). However, these are intransparent which excludes
their use for high-stakes financial decisions. To overcome this limitation, we
examine if a two-stage modeling approach can provide explainability. We combine
visual interpretability by Regression Activation Maps (RAM) for the CNN and a
linear regression for the overall prediction. Our experiments are based on 62.000
family homes in Philadelphia and the results indicate that the CNN learns aspects
related to vegetation and quality aspects of the house from exterior images, improving
the predictive accuracy of real estate appraisal by up to 5.4%.
author:
- first_name: Jan-Peter
full_name: Kucklick, Jan-Peter
id: '77066'
last_name: Kucklick
citation:
ama: 'Kucklick J-P. Visual Interpretability of Image-based Real Estate Appraisal.
In: 55th Annual Hawaii International Conference on System Sciences (HICSS-55).
; 2022.'
apa: Kucklick, J.-P. (2022). Visual Interpretability of Image-based Real Estate
Appraisal. 55th Annual Hawaii International Conference on System Sciences (HICSS-55).
Hawaii International Conference on System Science (HICSS), Virtual.
bibtex: '@inproceedings{Kucklick_2022, title={Visual Interpretability of Image-based
Real Estate Appraisal}, booktitle={55th Annual Hawaii International Conference
on System Sciences (HICSS-55)}, author={Kucklick, Jan-Peter}, year={2022} }'
chicago: Kucklick, Jan-Peter. “Visual Interpretability of Image-Based Real Estate
Appraisal.” In 55th Annual Hawaii International Conference on System Sciences
(HICSS-55), 2022.
ieee: J.-P. Kucklick, “Visual Interpretability of Image-based Real Estate Appraisal,”
presented at the Hawaii International Conference on System Science (HICSS), Virtual,
2022.
mla: Kucklick, Jan-Peter. “Visual Interpretability of Image-Based Real Estate Appraisal.”
55th Annual Hawaii International Conference on System Sciences (HICSS-55),
2022.
short: 'J.-P. Kucklick, in: 55th Annual Hawaii International Conference on System
Sciences (HICSS-55), 2022.'
conference:
end_date: 2022-01-07
location: Virtual
name: Hawaii International Conference on System Science (HICSS)
start_date: 2022-01-03
date_created: 2021-11-17T07:08:15Z
date_updated: 2022-01-06T06:57:40Z
department:
- _id: '195'
- _id: '196'
keyword:
- Explainable Artificial Intelligence (XAI)
- Regression Activation Maps
- Real Estate Appraisal
- Convolutional Block Attention Module
- Computer Vision
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://scholarspace.manoa.hawaii.edu/bitstream/10125/79519/0149.pdf
oa: '1'
publication: 55th Annual Hawaii International Conference on System Sciences (HICSS-55)
status: public
title: Visual Interpretability of Image-based Real Estate Appraisal
type: conference
user_id: '77066'
year: '2022'
...
---
_id: '27507'
abstract:
- lang: eng
text: Accurate real estate appraisal is essential in decision making processes of
financial institutions, governments, and trending real estate platforms like Zillow.
One of the most important factors of a property’s value is its location. However,
creating accurate quantifications of location remains a challenge. While traditional
approaches rely on Geographical Information Systems (GIS), recently unstructured
data in form of images was incorporated in the appraisal process, but text data
remains an untapped reservoir. Our study shows that using text data in form of
geolocated Wikipedia articles can increase predictive performance over traditional
GIS-based methods by 8.2% in spatial out-of-sample validation. A framework to
automatically extract geographically weighted vector representations for text
is established and used alongside traditional structural housing features to make
predictions and to uncover local patterns on sale price for real estate transactions
between 2015 and 2020 in Allegheny County, Pennsylvania.
author:
- first_name: Tim
full_name: Heuwinkel, Tim
last_name: Heuwinkel
- first_name: Jan-Peter
full_name: Kucklick, Jan-Peter
id: '77066'
last_name: Kucklick
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Heuwinkel T, Kucklick J-P, Müller O. Using Geolocated Text to Quantify Location
in Real Estate Appraisal. In: 55th Annual Hawaii International Conference on
System Sciences (HICSS-55). ; 2022.'
apa: Heuwinkel, T., Kucklick, J.-P., & Müller, O. (2022). Using Geolocated Text
to Quantify Location in Real Estate Appraisal. 55th Annual Hawaii International
Conference on System Sciences (HICSS-55). Hawaii International Conference
on System Science (HICSS), Virtual.
bibtex: '@inproceedings{Heuwinkel_Kucklick_Müller_2022, title={Using Geolocated
Text to Quantify Location in Real Estate Appraisal}, booktitle={55th Annual Hawaii
International Conference on System Sciences (HICSS-55)}, author={Heuwinkel, Tim
and Kucklick, Jan-Peter and Müller, Oliver}, year={2022} }'
chicago: Heuwinkel, Tim, Jan-Peter Kucklick, and Oliver Müller. “Using Geolocated
Text to Quantify Location in Real Estate Appraisal.” In 55th Annual Hawaii
International Conference on System Sciences (HICSS-55), 2022.
ieee: T. Heuwinkel, J.-P. Kucklick, and O. Müller, “Using Geolocated Text to Quantify
Location in Real Estate Appraisal,” presented at the Hawaii International Conference
on System Science (HICSS), Virtual, 2022.
mla: Heuwinkel, Tim, et al. “Using Geolocated Text to Quantify Location in Real
Estate Appraisal.” 55th Annual Hawaii International Conference on System Sciences
(HICSS-55), 2022.
short: 'T. Heuwinkel, J.-P. Kucklick, O. Müller, in: 55th Annual Hawaii International
Conference on System Sciences (HICSS-55), 2022.'
conference:
end_date: 2022-01-07
location: Virtual
name: Hawaii International Conference on System Science (HICSS)
start_date: 2022-01-03
date_created: 2021-11-17T07:12:03Z
date_updated: 2022-01-06T06:57:40Z
department:
- _id: '195'
keyword:
- Real Estate Appraisal
- Text Regression
- Natural Language Processing (NLP)
- Location Intelligence
- Wikipedia
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://scholarspace.manoa.hawaii.edu/bitstream/10125/80039/0561.pdf
oa: '1'
publication: 55th Annual Hawaii International Conference on System Sciences (HICSS-55)
status: public
title: Using Geolocated Text to Quantify Location in Real Estate Appraisal
type: conference
user_id: '77066'
year: '2022'
...
---
_id: '35620'
abstract:
- lang: eng
text: Deep learning models fuel many modern decision support systems, because they
typically provide high predictive performance. Among other domains, deep learning
is used in real-estate appraisal, where it allows to extend the analysis from
hard facts only (e.g., size, age) to also consider more implicit information about
the location or appearance of houses in the form of image data. However, one downside
of deep learning models is their intransparent mechanic of decision making, which
leads to a trade-off between accuracy and interpretability. This limits their
applicability for tasks where a justification of the decision is necessary. Therefore,
in this paper, we first combine different perspectives on interpretability into
a multi-dimensional framework for a socio-technical perspective on explainable
artificial intelligence. Second, we measure the performance gains of using multi-view
deep learning which leverages additional image data (satellite images) for real
estate appraisal. Third, we propose and test a novel post-hoc explainability method
called Grad-Ram. This modified version of Grad-Cam mitigates the intransparency
of convolutional neural networks (CNNs) for predicting continuous outcome variables.
With this, we try to reduce the accuracy-interpretability trade-off of multi-view
deep learning models. Our proposed network architecture outperforms traditional
hedonic regression models by 34% in terms of MAE. Furthermore, we find that the
used satellite images are the second most important predictor after square feet
in our model and that the network learns interpretable patterns about the neighborhood
structure and density.
article_type: original
author:
- first_name: Jan-Peter
full_name: Kucklick, Jan-Peter
id: '77066'
last_name: Kucklick
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Kucklick J-P, Müller O. Tackling the Accuracy–Interpretability Trade-off:
Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal.
ACM Transactions on Management Information Systems. Published online 2022.
doi:10.1145/3567430'
apa: 'Kucklick, J.-P., & Müller, O. (2022). Tackling the Accuracy–Interpretability
Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate
Appraisal. ACM Transactions on Management Information Systems. https://doi.org/10.1145/3567430'
bibtex: '@article{Kucklick_Müller_2022, title={Tackling the Accuracy–Interpretability
Trade-off: Interpretable Deep Learning Models for Satellite Image-based Real Estate
Appraisal}, DOI={10.1145/3567430},
journal={ACM Transactions on Management Information Systems}, publisher={Association
for Computing Machinery (ACM)}, author={Kucklick, Jan-Peter and Müller, Oliver},
year={2022} }'
chicago: 'Kucklick, Jan-Peter, and Oliver Müller. “Tackling the Accuracy–Interpretability
Trade-off: Interpretable Deep Learning Models for Satellite Image-Based Real Estate
Appraisal.” ACM Transactions on Management Information Systems, 2022. https://doi.org/10.1145/3567430.'
ieee: 'J.-P. Kucklick and O. Müller, “Tackling the Accuracy–Interpretability Trade-off:
Interpretable Deep Learning Models for Satellite Image-based Real Estate Appraisal,”
ACM Transactions on Management Information Systems, 2022, doi: 10.1145/3567430.'
mla: 'Kucklick, Jan-Peter, and Oliver Müller. “Tackling the Accuracy–Interpretability
Trade-off: Interpretable Deep Learning Models for Satellite Image-Based Real Estate
Appraisal.” ACM Transactions on Management Information Systems, Association
for Computing Machinery (ACM), 2022, doi:10.1145/3567430.'
short: J.-P. Kucklick, O. Müller, ACM Transactions on Management Information Systems
(2022).
date_created: 2023-01-10T05:16:02Z
date_updated: 2023-01-10T05:20:18Z
department:
- _id: '195'
- _id: '196'
doi: 10.1145/3567430
keyword:
- Interpretability
- Convolutional Neural Network
- Accuracy-Interpretability Trade-Of
- Real Estate Appraisal
- Hedonic Pricing
- Grad-Ram
language:
- iso: eng
main_file_link:
- url: https://dl.acm.org/doi/pdf/10.1145/3567430
publication: ACM Transactions on Management Information Systems
publication_identifier:
issn:
- 2158-656X
- 2158-6578
publication_status: published
publisher: Association for Computing Machinery (ACM)
status: public
title: 'Tackling the Accuracy–Interpretability Trade-off: Interpretable Deep Learning
Models for Satellite Image-based Real Estate Appraisal'
type: journal_article
user_id: '77066'
year: '2022'
...
---
_id: '33849'
abstract:
- lang: eng
text: Modern traffic control systems are key to cope with current and future traffic
challenges. In this paper information obtained from a microscopic traffic estimation
using various data sources is used to feed a new developed traffic control approach.
The presented method can control a traffic area with multiple traffic light systems
(TLS) reacting to individual road users and pedestrians. In contrast to widespread
green time extension techniques, this control selects the best phase sequence
by analyzing the current traffic state reconstructed in SUMO and its predicted
progress. To achieve this, the key aspect of the control strategy is to use Model
Predictive Control (MPC). In order to maintain realism for real world applications,
among other things, the traffic phase transitions are modelled in detail and integrated
within the prediction. For the efficiency, the approach incorporates a fuzzy logic
preselection of all phases reducing the computational effort. The evaluation itself
is able to be easily adjusted to focus on various objectives like low occupancies,
reducing waiting times and emissions, few number of phase transitions etc. determining
the best switching times for the selected phases. Exemplary traffic simulations
demonstrate the functionality of the MPC-based control and, in addition, some
aspects under development like the real-world communication network are also discussed.
author:
- first_name: Kevin
full_name: Malena, Kevin
id: '36303'
last_name: Malena
orcid: 0000-0003-1183-4679
- first_name: Christopher
full_name: Link, Christopher
id: '38249'
last_name: Link
- first_name: Leon
full_name: Bußemas, Leon
id: '51118'
last_name: Bußemas
- first_name: Sandra
full_name: Gausemeier, Sandra
id: '17793'
last_name: Gausemeier
- first_name: Ansgar
full_name: Trächtler, Ansgar
id: '552'
last_name: Trächtler
citation:
ama: 'Malena K, Link C, Bußemas L, Gausemeier S, Trächtler A. Traffic Estimation
and MPC-Based Traffic Light System Control in Realistic Real-Time Traffic Environments.
In: Klein C, Jarke M, Helfert M, Berns K, Gusikhin O, eds. Communications in
Computer and Information Science. Vol 1612. Communications in Computer and
Information Science. Springer International Publishing; 2022:232–254. doi:10.1007/978-3-031-17098-0_12'
apa: Malena, K., Link, C., Bußemas, L., Gausemeier, S., & Trächtler, A. (2022).
Traffic Estimation and MPC-Based Traffic Light System Control in Realistic Real-Time
Traffic Environments. In C. Klein, M. Jarke, M. Helfert, K. Berns, & O. Gusikhin
(Eds.), Communications in Computer and Information Science (Vol. 1612,
pp. 232–254). Springer International Publishing. https://doi.org/10.1007/978-3-031-17098-0_12
bibtex: '@inbook{Malena_Link_Bußemas_Gausemeier_Trächtler_2022, place={Cham}, series={Communications
in Computer and Information Science}, title={Traffic Estimation and MPC-Based
Traffic Light System Control in Realistic Real-Time Traffic Environments}, volume={1612},
DOI={10.1007/978-3-031-17098-0_12},
booktitle={Communications in Computer and Information Science}, publisher={Springer
International Publishing}, author={Malena, Kevin and Link, Christopher and Bußemas,
Leon and Gausemeier, Sandra and Trächtler, Ansgar}, editor={Klein, Cornel and
Jarke, Mathias and Helfert, Markus and Berns, Karsten and Gusikhin, Oleg}, year={2022},
pages={232–254}, collection={Communications in Computer and Information Science}
}'
chicago: 'Malena, Kevin, Christopher Link, Leon Bußemas, Sandra Gausemeier, and
Ansgar Trächtler. “Traffic Estimation and MPC-Based Traffic Light System Control
in Realistic Real-Time Traffic Environments.” In Communications in Computer
and Information Science, edited by Cornel Klein, Mathias Jarke, Markus Helfert,
Karsten Berns, and Oleg Gusikhin, 1612:232–254. Communications in Computer and
Information Science. Cham: Springer International Publishing, 2022. https://doi.org/10.1007/978-3-031-17098-0_12.'
ieee: 'K. Malena, C. Link, L. Bußemas, S. Gausemeier, and A. Trächtler, “Traffic
Estimation and MPC-Based Traffic Light System Control in Realistic Real-Time Traffic
Environments,” in Communications in Computer and Information Science, vol.
1612, C. Klein, M. Jarke, M. Helfert, K. Berns, and O. Gusikhin, Eds. Cham: Springer
International Publishing, 2022, pp. 232–254.'
mla: Malena, Kevin, et al. “Traffic Estimation and MPC-Based Traffic Light System
Control in Realistic Real-Time Traffic Environments.” Communications in Computer
and Information Science, edited by Cornel Klein et al., vol. 1612, Springer
International Publishing, 2022, pp. 232–254, doi:10.1007/978-3-031-17098-0_12.
short: 'K. Malena, C. Link, L. Bußemas, S. Gausemeier, A. Trächtler, in: C. Klein,
M. Jarke, M. Helfert, K. Berns, O. Gusikhin (Eds.), Communications in Computer
and Information Science, Springer International Publishing, Cham, 2022, pp. 232–254.'
date_created: 2022-10-20T15:06:39Z
date_updated: 2026-01-26T08:49:52Z
department:
- _id: '153'
doi: 10.1007/978-3-031-17098-0_12
editor:
- first_name: Cornel
full_name: Klein, Cornel
last_name: Klein
- first_name: Mathias
full_name: Jarke, Mathias
last_name: Jarke
- first_name: Markus
full_name: Helfert, Markus
last_name: Helfert
- first_name: Karsten
full_name: Berns, Karsten
last_name: Berns
- first_name: Oleg
full_name: Gusikhin, Oleg
last_name: Gusikhin
intvolume: ' 1612'
keyword:
- Traffic control
- Traffic estimation
- Real-time
- MPC
- Fuzzy
- Isolated intersection
- Networked intersection
- Sensor fusion
language:
- iso: eng
page: 232–254
place: Cham
publication: Communications in Computer and Information Science
publication_identifier:
isbn:
- '9783031170973'
- '9783031170980'
issn:
- 1865-0929
- 1865-0937
publication_status: published
publisher: Springer International Publishing
quality_controlled: '1'
related_material:
record:
- id: '24159'
relation: continues
status: public
series_title: Communications in Computer and Information Science
status: public
title: Traffic Estimation and MPC-Based Traffic Light System Control in Realistic
Real-Time Traffic Environments
type: book_chapter
user_id: '552'
volume: 1612
year: '2022'
...
---
_id: '22532'
abstract:
- lang: eng
text: In this publication, further elements of the newly developed inductive localization
in the near field are presented. The advantage of inductive localization is the
usage of the magnetic fields, which have a very low influence of non-metallic
materials in the environment and thus follows good applications in the area of
medicine and biochemistry. This allows a precise localization of sensor platforms
in inhomogeneous mixtures of materials, where classical methods have major problems
with inhomogeneous dielectric conductivity or density. The calculation of the
localization of the searched object differs from other methods such as ultrasound
or electromagnetic waves due to the source-free propagation of the magnetic field.
Therefore, new mathematical evaluation methods and systematic adaptations are
necessary, which are presented in this paper in circuit analysis. For this purpose,
the exact circuit influences of one coil and the influence of another coil are
investigated and which resonance circuit should be selected for both coils for
a inductive localization with optimized signal strength.
author:
- first_name: Sven
full_name: Lange, Sven
id: '38240'
last_name: Lange
- first_name: Christian
full_name: Hedayat, Christian
last_name: Hedayat
- first_name: Harald
full_name: Kuhn, Harald
last_name: Kuhn
- first_name: Ulrich
full_name: Hilleringmann, Ulrich
last_name: Hilleringmann
citation:
ama: 'Lange S, Hedayat C, Kuhn H, Hilleringmann U. Adaptation and Optimization of
Planar Coils for a More Accurate and Far-Reaching Magnetic Field-Based Localization
in the Near Field. In: 2021 Smart Systems Integration (SSI). Grenoble,
France: IEEE; 2021. doi:10.1109/ssi52265.2021.9466958'
apa: 'Lange, S., Hedayat, C., Kuhn, H., & Hilleringmann, U. (2021). Adaptation
and Optimization of Planar Coils for a More Accurate and Far-Reaching Magnetic
Field-Based Localization in the Near Field. In 2021 Smart Systems Integration
(SSI). Grenoble, France: IEEE. https://doi.org/10.1109/ssi52265.2021.9466958'
bibtex: '@inproceedings{Lange_Hedayat_Kuhn_Hilleringmann_2021, place={Grenoble,
France}, title={Adaptation and Optimization of Planar Coils for a More Accurate
and Far-Reaching Magnetic Field-Based Localization in the Near Field}, DOI={10.1109/ssi52265.2021.9466958},
booktitle={2021 Smart Systems Integration (SSI)}, publisher={IEEE}, author={Lange,
Sven and Hedayat, Christian and Kuhn, Harald and Hilleringmann, Ulrich}, year={2021}
}'
chicago: 'Lange, Sven, Christian Hedayat, Harald Kuhn, and Ulrich Hilleringmann.
“Adaptation and Optimization of Planar Coils for a More Accurate and Far-Reaching
Magnetic Field-Based Localization in the Near Field.” In 2021 Smart Systems
Integration (SSI). Grenoble, France: IEEE, 2021. https://doi.org/10.1109/ssi52265.2021.9466958.'
ieee: S. Lange, C. Hedayat, H. Kuhn, and U. Hilleringmann, “Adaptation and Optimization
of Planar Coils for a More Accurate and Far-Reaching Magnetic Field-Based Localization
in the Near Field,” in 2021 Smart Systems Integration (SSI), Grenoble,
France , 2021.
mla: Lange, Sven, et al. “Adaptation and Optimization of Planar Coils for a More
Accurate and Far-Reaching Magnetic Field-Based Localization in the Near Field.”
2021 Smart Systems Integration (SSI), IEEE, 2021, doi:10.1109/ssi52265.2021.9466958.
short: 'S. Lange, C. Hedayat, H. Kuhn, U. Hilleringmann, in: 2021 Smart Systems
Integration (SSI), IEEE, Grenoble, France, 2021.'
conference:
end_date: 2021-04-29
location: 'Grenoble, France '
name: 2021 Smart Systems Integration (SSI)
start_date: 2021-04-27
date_created: 2021-07-05T19:31:52Z
date_updated: 2022-01-06T06:55:36Z
department:
- _id: '59'
- _id: '485'
doi: 10.1109/ssi52265.2021.9466958
keyword:
- Electrotechnical Characteristics of Real Coils
- Inductive Localization
- Resonant Circuit
- Mutual Inductance
- Near-Field
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/document/9466958
place: Grenoble, France
publication: 2021 Smart Systems Integration (SSI)
publication_identifier:
isbn:
- '9781665440929'
publication_status: published
publisher: IEEE
status: public
title: Adaptation and Optimization of Planar Coils for a More Accurate and Far-Reaching
Magnetic Field-Based Localization in the Near Field
type: conference
user_id: '38240'
year: '2021'
...
---
_id: '48854'
abstract:
- lang: eng
text: We contribute to the theoretical understanding of randomized search heuristics
for dynamic problems. We consider the classical vertex coloring problem on graphs
and investigate the dynamic setting where edges are added to the current graph.
We then analyze the expected time for randomized search heuristics to recompute
high quality solutions. The (1+1) Evolutionary Algorithm and RLS operate in a
setting where the number of colors is bounded and we are minimizing the number
of conflicts. Iterated local search algorithms use an unbounded color palette
and aim to use the smallest colors and, consequently, the smallest number of colors.
We identify classes of bipartite graphs where reoptimization is as hard as or
even harder than optimization from scratch, i.e., starting with a random initialization.
Even adding a single edge can lead to hard symmetry problems. However, graph classes
that are hard for one algorithm turn out to be easy for others. In most cases
our bounds show that reoptimization is faster than optimizing from scratch. We
further show that tailoring mutation operators to parts of the graph where changes
have occurred can significantly reduce the expected reoptimization time. In most
settings the expected reoptimization time for such tailored algorithms is linear
in the number of added edges. However, tailored algorithms cannot prevent exponential
times in settings where the original algorithm is inefficient.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
- first_name: Pan
full_name: Peng, Pan
last_name: Peng
- first_name: Dirk
full_name: Sudholt, Dirk
last_name: Sudholt
citation:
ama: Bossek J, Neumann F, Peng P, Sudholt D. Time Complexity Analysis of Randomized
Search Heuristics for the Dynamic Graph Coloring Problem. Algorithmica.
2021;83(10):3148–3179. doi:10.1007/s00453-021-00838-3
apa: Bossek, J., Neumann, F., Peng, P., & Sudholt, D. (2021). Time Complexity
Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem.
Algorithmica, 83(10), 3148–3179. https://doi.org/10.1007/s00453-021-00838-3
bibtex: '@article{Bossek_Neumann_Peng_Sudholt_2021, title={Time Complexity Analysis
of Randomized Search Heuristics for the Dynamic Graph Coloring Problem}, volume={83},
DOI={10.1007/s00453-021-00838-3},
number={10}, journal={Algorithmica}, author={Bossek, Jakob and Neumann, Frank
and Peng, Pan and Sudholt, Dirk}, year={2021}, pages={3148–3179} }'
chicago: 'Bossek, Jakob, Frank Neumann, Pan Peng, and Dirk Sudholt. “Time Complexity
Analysis of Randomized Search Heuristics for the Dynamic Graph Coloring Problem.”
Algorithmica 83, no. 10 (2021): 3148–3179. https://doi.org/10.1007/s00453-021-00838-3.'
ieee: 'J. Bossek, F. Neumann, P. Peng, and D. Sudholt, “Time Complexity Analysis
of Randomized Search Heuristics for the Dynamic Graph Coloring Problem,” Algorithmica,
vol. 83, no. 10, pp. 3148–3179, 2021, doi: 10.1007/s00453-021-00838-3.'
mla: Bossek, Jakob, et al. “Time Complexity Analysis of Randomized Search Heuristics
for the Dynamic Graph Coloring Problem.” Algorithmica, vol. 83, no. 10,
2021, pp. 3148–3179, doi:10.1007/s00453-021-00838-3.
short: J. Bossek, F. Neumann, P. Peng, D. Sudholt, Algorithmica 83 (2021) 3148–3179.
date_created: 2023-11-14T15:58:54Z
date_updated: 2023-12-13T10:51:34Z
department:
- _id: '819'
doi: 10.1007/s00453-021-00838-3
intvolume: ' 83'
issue: '10'
keyword:
- Dynamic optimization
- Evolutionary algorithms
- Running time analysis
language:
- iso: eng
page: 3148–3179
publication: Algorithmica
publication_identifier:
issn:
- 0178-4617
status: public
title: Time Complexity Analysis of Randomized Search Heuristics for the Dynamic Graph
Coloring Problem
type: journal_article
user_id: '102979'
volume: 83
year: '2021'
...
---
_id: '24547'
abstract:
- lang: eng
text: 'Over the last years, several approaches for the data-driven estimation of
expected possession value (EPV) in basketball and association football (soccer)
have been proposed. In this paper, we develop and evaluate PIVOT: the first such
framework for team handball. Accounting for the fast-paced, dynamic nature and
relative data scarcity of hand- ball, we propose a parsimonious end-to-end deep
learning architecture that relies solely on tracking data. This efficient approach
is capable of predicting the probability that a team will score within the near
future given the fine-grained spatio-temporal distribution of all players and
the ball over the last seconds of the game. Our experiments indicate that PIVOT
is able to produce accurate and calibrated probability estimates, even when trained
on a relatively small dataset. We also showcase two interactive applications of
PIVOT for valuing actual and counterfactual player decisions and actions in real-time.'
author:
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
- first_name: Matthew
full_name: Caron, Matthew
id: '60721'
last_name: Caron
- first_name: Michael
full_name: Döring, Michael
last_name: Döring
- first_name: Tim
full_name: Heuwinkel, Tim
last_name: Heuwinkel
- first_name: Jochen
full_name: Baumeister, Jochen
id: '46'
last_name: Baumeister
orcid: 0000-0003-2683-5826
citation:
ama: 'Müller O, Caron M, Döring M, Heuwinkel T, Baumeister J. PIVOT: A Parsimonious
End-to-End Learning Framework for Valuing Player Actions in Handball using Tracking
Data. In: 8th Workshop on Machine Learning and Data Mining for Sports Analytics
(ECML PKDD 2021).'
apa: 'Müller, O., Caron, M., Döring, M., Heuwinkel, T., & Baumeister, J. (n.d.).
PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions
in Handball using Tracking Data. 8th Workshop on Machine Learning and Data
Mining for Sports Analytics (ECML PKDD 2021). European Conference on Machine
Learning and Principles and Practice of Knowledge Discovery (ECML PKDD 2021),
Online.'
bibtex: '@inproceedings{Müller_Caron_Döring_Heuwinkel_Baumeister, title={PIVOT:
A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball
using Tracking Data}, booktitle={8th Workshop on Machine Learning and Data Mining
for Sports Analytics (ECML PKDD 2021)}, author={Müller, Oliver and Caron, Matthew
and Döring, Michael and Heuwinkel, Tim and Baumeister, Jochen} }'
chicago: 'Müller, Oliver, Matthew Caron, Michael Döring, Tim Heuwinkel, and Jochen
Baumeister. “PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player
Actions in Handball Using Tracking Data.” In 8th Workshop on Machine Learning
and Data Mining for Sports Analytics (ECML PKDD 2021), n.d.'
ieee: 'O. Müller, M. Caron, M. Döring, T. Heuwinkel, and J. Baumeister, “PIVOT:
A Parsimonious End-to-End Learning Framework for Valuing Player Actions in Handball
using Tracking Data,” presented at the European Conference on Machine Learning
and Principles and Practice of Knowledge Discovery (ECML PKDD 2021), Online.'
mla: 'Müller, Oliver, et al. “PIVOT: A Parsimonious End-to-End Learning Framework
for Valuing Player Actions in Handball Using Tracking Data.” 8th Workshop on
Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021).'
short: 'O. Müller, M. Caron, M. Döring, T. Heuwinkel, J. Baumeister, in: 8th Workshop
on Machine Learning and Data Mining for Sports Analytics (ECML PKDD 2021), n.d.'
conference:
end_date: 2021-09-17
location: Online
name: European Conference on Machine Learning and Principles and Practice of Knowledge
Discovery (ECML PKDD 2021)
start_date: 2021-09-13
date_created: 2021-09-16T08:33:04Z
date_updated: 2023-02-28T08:58:24Z
department:
- _id: '196'
- _id: '172'
keyword:
- expected possession value
- handball
- tracking data
- time series classification
- deep learning
language:
- iso: eng
main_file_link:
- url: https://dtai.cs.kuleuven.be/events/MLSA21/papers/MLSA21_paper_muller.pdf
publication: 8th Workshop on Machine Learning and Data Mining for Sports Analytics
(ECML PKDD 2021)
publication_status: inpress
status: public
title: 'PIVOT: A Parsimonious End-to-End Learning Framework for Valuing Player Actions
in Handball using Tracking Data'
type: conference
user_id: '60721'
year: '2021'
...
---
_id: '27111'
abstract:
- lang: eng
text: In the industry 4.0 era, there is a growing need to transform unstructured
data acquired by a multitude of sources into information and subsequently into
knowledge to improve the quality of manufactured products, to boost production,
for predictive maintenance, etc. Data-driven approaches, such as machine learning
techniques, are typically employed to model the underlying relationship from data.
However, an increase in model accuracy with state-of-the-art methods, such as
deep convolutional neural networks, results in less interpretability and transparency.
Due to the ease of implementation, interpretation and transparency to both domain
experts and non-experts, a rule-based method is proposed in this paper, for prognostics
and health management (PHM) and specifically for diagnostics. The proposed method
utilizes the most relevant sensor signals acquired via feature extraction and
selection techniques and expert knowledge. As a case study, the presented method
is evaluated on data from a real-world quality control set-up provided by the
European prognostics and health management society (PHME) at the conference’s
2021 data challenge. With the proposed method, our team took the third place,
capable of successfully diagnosing different fault modes, irrespective of varying
conditions.
author:
- first_name: Osarenren Kennedy
full_name: Aimiyekagbon, Osarenren Kennedy
id: '9557'
last_name: Aimiyekagbon
- first_name: Lars
full_name: Muth, Lars
id: '77313'
last_name: Muth
orcid: 0000-0002-2938-5616
- first_name: Meike Claudia
full_name: Wohlleben, Meike Claudia
id: '43991'
last_name: Wohlleben
orcid: 0009-0009-9767-7168
- first_name: Amelie
full_name: Bender, Amelie
id: '54290'
last_name: Bender
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Aimiyekagbon OK, Muth L, Wohlleben MC, Bender A, Sextro W. Rule-based Diagnostics
of a Production Line. In: Do P, King S, Fink O, eds. Proceedings of the European
Conference of the PHM Society 2021. Vol 6. ; 2021:527-536. doi:10.36001/phme.2021.v6i1.3042'
apa: Aimiyekagbon, O. K., Muth, L., Wohlleben, M. C., Bender, A., & Sextro,
W. (2021). Rule-based Diagnostics of a Production Line. In P. Do, S. King, &
O. Fink (Eds.), Proceedings of the European Conference of the PHM Society 2021
(Vol. 6, Issue 1, pp. 527–536). https://doi.org/10.36001/phme.2021.v6i1.3042
bibtex: '@inproceedings{Aimiyekagbon_Muth_Wohlleben_Bender_Sextro_2021, title={Rule-based
Diagnostics of a Production Line}, volume={6}, DOI={10.36001/phme.2021.v6i1.3042},
number={1}, booktitle={Proceedings of the European Conference of the PHM Society
2021}, author={Aimiyekagbon, Osarenren Kennedy and Muth, Lars and Wohlleben, Meike
Claudia and Bender, Amelie and Sextro, Walter}, editor={Do, Phuc and King, Steve
and Fink, Olga}, year={2021}, pages={527–536} }'
chicago: Aimiyekagbon, Osarenren Kennedy, Lars Muth, Meike Claudia Wohlleben, Amelie
Bender, and Walter Sextro. “Rule-Based Diagnostics of a Production Line.” In Proceedings
of the European Conference of the PHM Society 2021, edited by Phuc Do, Steve
King, and Olga Fink, 6:527–36, 2021. https://doi.org/10.36001/phme.2021.v6i1.3042.
ieee: 'O. K. Aimiyekagbon, L. Muth, M. C. Wohlleben, A. Bender, and W. Sextro, “Rule-based
Diagnostics of a Production Line,” in Proceedings of the European Conference
of the PHM Society 2021, 2021, vol. 6, no. 1, pp. 527–536, doi: 10.36001/phme.2021.v6i1.3042.'
mla: Aimiyekagbon, Osarenren Kennedy, et al. “Rule-Based Diagnostics of a Production
Line.” Proceedings of the European Conference of the PHM Society 2021,
edited by Phuc Do et al., vol. 6, no. 1, 2021, pp. 527–36, doi:10.36001/phme.2021.v6i1.3042.
short: 'O.K. Aimiyekagbon, L. Muth, M.C. Wohlleben, A. Bender, W. Sextro, in: P.
Do, S. King, O. Fink (Eds.), Proceedings of the European Conference of the PHM
Society 2021, 2021, pp. 527–536.'
conference:
name: PHM Society European Conference
date_created: 2021-11-03T12:26:39Z
date_updated: 2023-09-22T09:13:01Z
department:
- _id: '151'
doi: 10.36001/phme.2021.v6i1.3042
editor:
- first_name: Phuc
full_name: Do, Phuc
last_name: Do
- first_name: Steve
full_name: King, Steve
last_name: King
- first_name: Olga
full_name: Fink, Olga
last_name: Fink
intvolume: ' 6'
issue: '1'
keyword:
- PHME 2021
- Feature Selection Classification
- Feature Selection Clustering
- Interpretable Model
- Transparent Model
- Industry 4.0
- Real-World Diagnostics
- Quality Control
- Predictive Maintenance
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://papers.phmsociety.org/index.php/phme/article/download/3042/1812
oa: '1'
page: 527-536
publication: Proceedings of the European Conference of the PHM Society 2021
publication_status: published
quality_controlled: '1'
status: public
title: Rule-based Diagnostics of a Production Line
type: conference
user_id: '9557'
volume: 6
year: '2021'
...
---
_id: '48847'
abstract:
- lang: eng
text: Dynamic optimization problems have gained significant attention in evolutionary
computation as evolutionary algorithms (EAs) can easily adapt to changing environments.
We show that EAs can solve the graph coloring problem for bipartite graphs more
efficiently by using dynamic optimization. In our approach the graph instance
is given incrementally such that the EA can reoptimize its coloring when a new
edge introduces a conflict. We show that, when edges are inserted in a way that
preserves graph connectivity, Randomized Local Search (RLS) efficiently finds
a proper 2-coloring for all bipartite graphs. This includes graphs for which RLS
and other EAs need exponential expected time in a static optimization scenario.
We investigate different ways of building up the graph by popular graph traversals
such as breadth-first-search and depth-first-search and analyse the resulting
runtime behavior. We further show that offspring populations (e. g. a (1 + {$\lambda$})
RLS) lead to an exponential speedup in {$\lambda$}. Finally, an island model using
3 islands succeeds in an optimal time of {$\Theta$}(m) on every m-edge bipartite
graph, outperforming offspring populations. This is the first example where an
island model guarantees a speedup that is not bounded in the number of islands.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
- first_name: Pan
full_name: Peng, Pan
last_name: Peng
- first_name: Dirk
full_name: Sudholt, Dirk
last_name: Sudholt
citation:
ama: 'Bossek J, Neumann F, Peng P, Sudholt D. More Effective Randomized Search Heuristics
for Graph Coloring through Dynamic Optimization. In: Proceedings of the Genetic
and Evolutionary Computation Conference. GECCO ’20. Association for Computing
Machinery; 2020:1277–1285. doi:10.1145/3377930.3390174'
apa: Bossek, J., Neumann, F., Peng, P., & Sudholt, D. (2020). More Effective
Randomized Search Heuristics for Graph Coloring through Dynamic Optimization.
Proceedings of the Genetic and Evolutionary Computation Conference, 1277–1285.
https://doi.org/10.1145/3377930.3390174
bibtex: '@inproceedings{Bossek_Neumann_Peng_Sudholt_2020, place={New York, NY, USA},
series={GECCO ’20}, title={More Effective Randomized Search Heuristics for Graph
Coloring through Dynamic Optimization}, DOI={10.1145/3377930.3390174},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Bossek, Jakob and Neumann,
Frank and Peng, Pan and Sudholt, Dirk}, year={2020}, pages={1277–1285}, collection={GECCO
’20} }'
chicago: 'Bossek, Jakob, Frank Neumann, Pan Peng, and Dirk Sudholt. “More Effective
Randomized Search Heuristics for Graph Coloring through Dynamic Optimization.”
In Proceedings of the Genetic and Evolutionary Computation Conference,
1277–1285. GECCO ’20. New York, NY, USA: Association for Computing Machinery,
2020. https://doi.org/10.1145/3377930.3390174.'
ieee: 'J. Bossek, F. Neumann, P. Peng, and D. Sudholt, “More Effective Randomized
Search Heuristics for Graph Coloring through Dynamic Optimization,” in Proceedings
of the Genetic and Evolutionary Computation Conference, 2020, pp. 1277–1285,
doi: 10.1145/3377930.3390174.'
mla: Bossek, Jakob, et al. “More Effective Randomized Search Heuristics for Graph
Coloring through Dynamic Optimization.” Proceedings of the Genetic and Evolutionary
Computation Conference, Association for Computing Machinery, 2020, pp. 1277–1285,
doi:10.1145/3377930.3390174.
short: 'J. Bossek, F. Neumann, P. Peng, D. Sudholt, in: Proceedings of the Genetic
and Evolutionary Computation Conference, Association for Computing Machinery,
New York, NY, USA, 2020, pp. 1277–1285.'
date_created: 2023-11-14T15:58:53Z
date_updated: 2023-12-13T10:43:41Z
department:
- _id: '819'
doi: 10.1145/3377930.3390174
extern: '1'
keyword:
- dynamic optimization
- evolutionary algorithms
- running time analysis
- theory
language:
- iso: eng
page: 1277–1285
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-7128-5
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’20
status: public
title: More Effective Randomized Search Heuristics for Graph Coloring through Dynamic
Optimization
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '48851'
abstract:
- lang: eng
text: Several important optimization problems in the area of vehicle routing can
be seen as variants of the classical Traveling Salesperson Problem (TSP). In the
area of evolutionary computation, the Traveling Thief Problem (TTP) has gained
increasing interest over the last 5 years. In this paper, we investigate the effect
of weights on such problems, in the sense that the cost of traveling increases
with respect to the weights of nodes already visited during a tour. This provides
abstractions of important TSP variants such as the Traveling Thief Problem and
time dependent TSP variants, and allows to study precisely the increase in difficulty
caused by weight dependence. We provide a 3.59-approximation for this weight dependent
version of TSP with metric distances and bounded positive weights. Furthermore,
we conduct experimental investigations for simple randomized local search with
classical mutation operators and two variants of the state-of-the-art evolutionary
algorithm EAX adapted to the weighted TSP. Our results show the impact of the
node weights on the position of the nodes in the resulting tour.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Katrin
full_name: Casel, Katrin
last_name: Casel
- first_name: Pascal
full_name: Kerschke, Pascal
last_name: Kerschke
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
citation:
ama: 'Bossek J, Casel K, Kerschke P, Neumann F. The Node Weight Dependent Traveling
Salesperson Problem: Approximation Algorithms and Randomized Search Heuristics.
In: Proceedings of the Genetic and Evolutionary Computation Conference.
GECCO ’20. Association for Computing Machinery; 2020:1286–1294. doi:10.1145/3377930.3390243'
apa: 'Bossek, J., Casel, K., Kerschke, P., & Neumann, F. (2020). The Node Weight
Dependent Traveling Salesperson Problem: Approximation Algorithms and Randomized
Search Heuristics. Proceedings of the Genetic and Evolutionary Computation
Conference, 1286–1294. https://doi.org/10.1145/3377930.3390243'
bibtex: '@inproceedings{Bossek_Casel_Kerschke_Neumann_2020, place={New York, NY,
USA}, series={GECCO ’20}, title={The Node Weight Dependent Traveling Salesperson
Problem: Approximation Algorithms and Randomized Search Heuristics}, DOI={10.1145/3377930.3390243},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Bossek, Jakob and Casel,
Katrin and Kerschke, Pascal and Neumann, Frank}, year={2020}, pages={1286–1294},
collection={GECCO ’20} }'
chicago: 'Bossek, Jakob, Katrin Casel, Pascal Kerschke, and Frank Neumann. “The
Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms
and Randomized Search Heuristics.” In Proceedings of the Genetic and Evolutionary
Computation Conference, 1286–1294. GECCO ’20. New York, NY, USA: Association
for Computing Machinery, 2020. https://doi.org/10.1145/3377930.3390243.'
ieee: 'J. Bossek, K. Casel, P. Kerschke, and F. Neumann, “The Node Weight Dependent
Traveling Salesperson Problem: Approximation Algorithms and Randomized Search
Heuristics,” in Proceedings of the Genetic and Evolutionary Computation Conference,
2020, pp. 1286–1294, doi: 10.1145/3377930.3390243.'
mla: 'Bossek, Jakob, et al. “The Node Weight Dependent Traveling Salesperson Problem:
Approximation Algorithms and Randomized Search Heuristics.” Proceedings of
the Genetic and Evolutionary Computation Conference, Association for Computing
Machinery, 2020, pp. 1286–1294, doi:10.1145/3377930.3390243.'
short: 'J. Bossek, K. Casel, P. Kerschke, F. Neumann, in: Proceedings of the Genetic
and Evolutionary Computation Conference, Association for Computing Machinery,
New York, NY, USA, 2020, pp. 1286–1294.'
date_created: 2023-11-14T15:58:53Z
date_updated: 2023-12-13T10:43:33Z
department:
- _id: '819'
doi: 10.1145/3377930.3390243
extern: '1'
keyword:
- dynamic optimization
- evolutionary algorithms
- running time analysis
- theory
language:
- iso: eng
page: 1286–1294
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-7128-5
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’20
status: public
title: 'The Node Weight Dependent Traveling Salesperson Problem: Approximation Algorithms
and Randomized Search Heuristics'
type: conference
user_id: '102979'
year: '2020'
...
---
_id: '17156'
abstract:
- lang: eng
text: Business Process Management is a boundary-spanning discipline that aligns
operational capabilities and technology to design and manage business processes.
The Digital Transformation has enabled human actors, information systems, and
smart products to interact with each other via multiple digital channels. The
emergence of this hyper-connected world greatly leverages the prospects of business
processes – but also boosts their complexity to a new level. We need to discuss
how the BPM discipline can find new ways for identifying, analyzing, designing,
implementing, executing, and monitoring business processes. In this research note,
selected transformative trends are explored and their impact on current theories
and IT artifacts in the BPM discipline is discussed to stimulate transformative
thinking and prospective research in this field.
article_type: original
author:
- first_name: Daniel
full_name: Beverungen, Daniel
id: '59677'
last_name: Beverungen
- first_name: Joos C. A. M.
full_name: Buijs, Joos C. A. M.
last_name: Buijs
- first_name: Jörg
full_name: Becker, Jörg
last_name: Becker
- first_name: Claudio
full_name: Di Ciccio, Claudio
last_name: Di Ciccio
- first_name: Wil M. P.
full_name: van der Aalst, Wil M. P.
last_name: van der Aalst
- first_name: Christian
full_name: Bartelheimer, Christian
id: '49160'
last_name: Bartelheimer
- first_name: Jan
full_name: vom Brocke, Jan
last_name: vom Brocke
- first_name: Marco
full_name: Comuzzi, Marco
last_name: Comuzzi
- first_name: Karsten
full_name: Kraume, Karsten
last_name: Kraume
- first_name: Henrik
full_name: Leopold, Henrik
last_name: Leopold
- first_name: Martin
full_name: Matzner, Martin
last_name: Matzner
- first_name: Jan
full_name: Mendling, Jan
last_name: Mendling
- first_name: Nadine
full_name: Ogonek, Nadine
last_name: Ogonek
- first_name: Till
full_name: Post, Till
last_name: Post
- first_name: Manuel
full_name: Resinas, Manuel
last_name: Resinas
- first_name: Kate
full_name: Revoredo, Kate
last_name: Revoredo
- first_name: Adela
full_name: del-Río-Ortega, Adela
last_name: del-Río-Ortega
- first_name: Marcello
full_name: La Rosa, Marcello
last_name: La Rosa
- first_name: Flávia Maria
full_name: Santoro, Flávia Maria
last_name: Santoro
- first_name: Andreas
full_name: Solti, Andreas
last_name: Solti
- first_name: Minseok
full_name: Song, Minseok
last_name: Song
- first_name: Armin
full_name: Stein, Armin
last_name: Stein
- first_name: Matthias
full_name: Stierle, Matthias
last_name: Stierle
- first_name: Verena
full_name: Wolf, Verena
id: '23633'
last_name: Wolf
citation:
ama: Beverungen D, Buijs JCAM, Becker J, et al. Seven Paradoxes of Business Process
Management in a Hyper-Connected World. Business & Information Systems Engineering.
2020;63:145-156. doi:10.1007/s12599-020-00646-z
apa: Beverungen, D., Buijs, J. C. A. M., Becker, J., Di Ciccio, C., van der Aalst,
W. M. P., Bartelheimer, C., vom Brocke, J., Comuzzi, M., Kraume, K., Leopold,
H., Matzner, M., Mendling, J., Ogonek, N., Post, T., Resinas, M., Revoredo, K.,
del-Río-Ortega, A., La Rosa, M., Santoro, F. M., … Wolf, V. (2020). Seven Paradoxes
of Business Process Management in a Hyper-Connected World. Business & Information
Systems Engineering, 63, 145–156. https://doi.org/10.1007/s12599-020-00646-z
bibtex: '@article{Beverungen_Buijs_Becker_Di Ciccio_van der Aalst_Bartelheimer_vom
Brocke_Comuzzi_Kraume_Leopold_et al._2020, title={Seven Paradoxes of Business
Process Management in a Hyper-Connected World}, volume={63}, DOI={10.1007/s12599-020-00646-z},
journal={Business & Information Systems Engineering}, publisher={SpringerNature},
author={Beverungen, Daniel and Buijs, Joos C. A. M. and Becker, Jörg and Di Ciccio,
Claudio and van der Aalst, Wil M. P. and Bartelheimer, Christian and vom Brocke,
Jan and Comuzzi, Marco and Kraume, Karsten and Leopold, Henrik and et al.}, year={2020},
pages={145–156} }'
chicago: 'Beverungen, Daniel, Joos C. A. M. Buijs, Jörg Becker, Claudio Di Ciccio,
Wil M. P. van der Aalst, Christian Bartelheimer, Jan vom Brocke, et al. “Seven
Paradoxes of Business Process Management in a Hyper-Connected World.” Business
& Information Systems Engineering 63 (2020): 145–56. https://doi.org/10.1007/s12599-020-00646-z.'
ieee: 'D. Beverungen et al., “Seven Paradoxes of Business Process Management
in a Hyper-Connected World,” Business & Information Systems Engineering,
vol. 63, pp. 145–156, 2020, doi: 10.1007/s12599-020-00646-z.'
mla: Beverungen, Daniel, et al. “Seven Paradoxes of Business Process Management
in a Hyper-Connected World.” Business & Information Systems Engineering,
vol. 63, SpringerNature, 2020, pp. 145–56, doi:10.1007/s12599-020-00646-z.
short: D. Beverungen, J.C.A.M. Buijs, J. Becker, C. Di Ciccio, W.M.P. van der Aalst,
C. Bartelheimer, J. vom Brocke, M. Comuzzi, K. Kraume, H. Leopold, M. Matzner,
J. Mendling, N. Ogonek, T. Post, M. Resinas, K. Revoredo, A. del-Río-Ortega, M.
La Rosa, F.M. Santoro, A. Solti, M. Song, A. Stein, M. Stierle, V. Wolf, Business
& Information Systems Engineering 63 (2020) 145–156.
date_created: 2020-06-24T10:40:45Z
date_updated: 2024-04-18T12:50:57Z
ddc:
- '380'
department:
- _id: '526'
doi: 10.1007/s12599-020-00646-z
file:
- access_level: closed
content_type: application/pdf
creator: dabe
date_created: 2024-04-18T12:49:25Z
date_updated: 2024-04-18T12:49:25Z
file_id: '53574'
file_name: Business_Process_Management_in_a_Hyperconnected_World.pdf
file_size: 360869
relation: main_file
success: 1
file_date_updated: 2024-04-18T12:49:25Z
has_accepted_license: '1'
intvolume: ' 63'
keyword:
- Business process management (BPM)
- Social computing
- Smart devices
- Big data analytics
- Real-time computing
- BPM life-cycle
language:
- iso: eng
page: 145-156
project:
- _id: '1070'
call_identifier: MSCA-RISE-2014
grant_number: '645751'
name: 'RISE_BPM: Propelling Business Process Management by Research and Innovation
Staff Exchange'
publication: Business & Information Systems Engineering
publication_identifier:
issn:
- 2363-7005
- 1867-0202
publication_status: published
publisher: SpringerNature
quality_controlled: '1'
status: public
title: Seven Paradoxes of Business Process Management in a Hyper-Connected World
type: journal_article
user_id: '59677'
volume: 63
year: '2020'
...
---
_id: '17810'
abstract:
- lang: eng
text: In all fields, the significance of a reliable and accurate predictive model
is almost unquantifiable. With deep domain knowledge, models derived from first
principles typically outperforms other models in terms of reliability and accuracy.
When it may become a cumbersome or an unachievable task to build or validate such
models of complex (non-linear) systems, machine learning techniques are employed
to build predictive models. However, the accuracy of such techniques is not only
dependent on the hyper-parameters of the chosen algorithm, but also on the amount
and quality of data. This paper investigates the application of classical time
series forecasting approaches for the reliable prognostics of technical systems,
where black box machine learning techniques might not successfully be employed
given insufficient amount of data and where first principles models are infeasible
due to lack of domain specific data. Forecasting by analogy, forecasting by analytical
function fitting, an exponential smoothing forecasting method and the long short-term
memory (LSTM) are evaluated and compared against the ground truth data. As a case
study, the methods are applied to predict future crack lengths of riveted aluminium
plates under cyclic loading. The performance of the predictive models is evaluated
based on error metrics leading to a proposal of when to apply which forecasting
approach.
author:
- first_name: Osarenren Kennedy
full_name: Aimiyekagbon, Osarenren Kennedy
id: '9557'
last_name: Aimiyekagbon
- first_name: Amelie
full_name: Bender, Amelie
id: '54290'
last_name: Bender
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
citation:
ama: 'Aimiyekagbon OK, Bender A, Sextro W. Evaluation of time series forecasting
approaches for the reliable crack length prediction of riveted aluminium plates
given insufficient data. In: PHM Society European Conference. Vol 5. ;
2020.'
apa: Aimiyekagbon, O. K., Bender, A., & Sextro, W. (2020). Evaluation of time
series forecasting approaches for the reliable crack length prediction of riveted
aluminium plates given insufficient data. PHM Society European Conference,
5(1).
bibtex: '@inproceedings{Aimiyekagbon_Bender_Sextro_2020, title={Evaluation of time
series forecasting approaches for the reliable crack length prediction of riveted
aluminium plates given insufficient data}, volume={5}, number={1}, booktitle={PHM
Society European Conference}, author={Aimiyekagbon, Osarenren Kennedy and Bender,
Amelie and Sextro, Walter}, year={2020} }'
chicago: Aimiyekagbon, Osarenren Kennedy, Amelie Bender, and Walter Sextro. “Evaluation
of Time Series Forecasting Approaches for the Reliable Crack Length Prediction
of Riveted Aluminium Plates given Insufficient Data.” In PHM Society European
Conference, Vol. 5, 2020.
ieee: O. K. Aimiyekagbon, A. Bender, and W. Sextro, “Evaluation of time series forecasting
approaches for the reliable crack length prediction of riveted aluminium plates
given insufficient data,” in PHM Society European Conference, 2020, vol.
5, no. 1.
mla: Aimiyekagbon, Osarenren Kennedy, et al. “Evaluation of Time Series Forecasting
Approaches for the Reliable Crack Length Prediction of Riveted Aluminium Plates
given Insufficient Data.” PHM Society European Conference, vol. 5, no.
1, 2020.
short: 'O.K. Aimiyekagbon, A. Bender, W. Sextro, in: PHM Society European Conference,
2020.'
date_created: 2020-08-11T13:32:40Z
date_updated: 2023-09-22T09:13:16Z
department:
- _id: '151'
intvolume: ' 5'
issue: '1'
keyword:
- PHM 2019
- crack propagation
- forecasting
- unevenly spaced time series
- step ahead prediction
- short time series
language:
- iso: eng
publication: PHM Society European Conference
quality_controlled: '1'
status: public
title: Evaluation of time series forecasting approaches for the reliable crack length
prediction of riveted aluminium plates given insufficient data
type: conference
user_id: '9557'
volume: 5
year: '2020'
...
---
_id: '15488'
abstract:
- lang: eng
text: The continuous refinement of sensor technologies enables the manufacturing
industry to capture increasing amounts of data during the production process.
As processes take time to complete, sensors register large amounts of time-series-like
data for each product. In order to make this data usable, a feature extraction
is mandatory. In this work, we discuss and evaluate different network architectures,
input pre-processing and cost functions regarding, among other aspects, their
suitability for time series of different lengths.
author:
- first_name: Christian
full_name: Thiel, Christian
last_name: Thiel
- first_name: Carolin
full_name: Steidl, Carolin
last_name: Steidl
- first_name: Bernd
full_name: Henning, Bernd
id: '213'
last_name: Henning
citation:
ama: 'Thiel C, Steidl C, Henning B. P2.9 Comparison of deep feature extraction techniques
for varying-length time series from an industrial piercing press. In: AMA Service
GmbH, ed. 20. GMA/ITG-Fachtagung. Sensoren Und Messsysteme 2019. Von-Münchhausen-Str.
49, 31515 Wunstorf; 2019. doi:10.5162/SENSOREN2019/P2.9'
apa: Thiel, C., Steidl, C., & Henning, B. (2019). P2.9 Comparison of deep feature
extraction techniques for varying-length time series from an industrial piercing
press. In AMA Service GmbH (Ed.), 20. GMA/ITG-Fachtagung. Sensoren und Messsysteme
2019. Von-Münchhausen-Str. 49, 31515 Wunstorf. https://doi.org/10.5162/SENSOREN2019/P2.9
bibtex: '@inproceedings{Thiel_Steidl_Henning_2019, place={Von-Münchhausen-Str. 49,
31515 Wunstorf}, title={P2.9 Comparison of deep feature extraction techniques
for varying-length time series from an industrial piercing press}, DOI={10.5162/SENSOREN2019/P2.9},
booktitle={20. GMA/ITG-Fachtagung. Sensoren und Messsysteme 2019}, author={Thiel,
Christian and Steidl, Carolin and Henning, Bernd}, editor={AMA Service GmbHEditor},
year={2019} }'
chicago: Thiel, Christian, Carolin Steidl, and Bernd Henning. “P2.9 Comparison of
Deep Feature Extraction Techniques for Varying-Length Time Series from an Industrial
Piercing Press.” In 20. GMA/ITG-Fachtagung. Sensoren Und Messsysteme 2019,
edited by AMA Service GmbH. Von-Münchhausen-Str. 49, 31515 Wunstorf, 2019. https://doi.org/10.5162/SENSOREN2019/P2.9.
ieee: C. Thiel, C. Steidl, and B. Henning, “P2.9 Comparison of deep feature extraction
techniques for varying-length time series from an industrial piercing press,”
in 20. GMA/ITG-Fachtagung. Sensoren und Messsysteme 2019, 2019.
mla: Thiel, Christian, et al. “P2.9 Comparison of Deep Feature Extraction Techniques
for Varying-Length Time Series from an Industrial Piercing Press.” 20. GMA/ITG-Fachtagung.
Sensoren Und Messsysteme 2019, edited by AMA Service GmbH, 2019, doi:10.5162/SENSOREN2019/P2.9.
short: 'C. Thiel, C. Steidl, B. Henning, in: AMA Service GmbH (Ed.), 20. GMA/ITG-Fachtagung.
Sensoren Und Messsysteme 2019, Von-Münchhausen-Str. 49, 31515 Wunstorf, 2019.'
corporate_editor:
- AMA Service GmbH
date_created: 2020-01-10T16:03:58Z
date_updated: 2022-01-06T06:52:27Z
department:
- _id: '49'
doi: 10.5162/SENSOREN2019/P2.9
keyword:
- Dynamic Time Warping
- Feature Extraction
- Masking
- Neural Networks
language:
- iso: eng
place: Von-Münchhausen-Str. 49, 31515 Wunstorf
publication: 20. GMA/ITG-Fachtagung. Sensoren und Messsysteme 2019
publication_identifier:
isbn:
- 978-3-9819376-0-2
status: public
title: P2.9 Comparison of deep feature extraction techniques for varying-length time
series from an industrial piercing press
type: conference
user_id: '11829'
year: '2019'
...
---
_id: '48843'
abstract:
- lang: eng
text: We contribute to the theoretical understanding of randomized search heuristics
for dynamic problems. We consider the classical graph coloring problem and investigate
the dynamic setting where edges are added to the current graph. We then analyze
the expected time for randomized search heuristics to recompute high quality solutions.
This includes the (1+1) EA and RLS in a setting where the number of colors is
bounded and we are minimizing the number of conflicts as well as iterated local
search algorithms that use an unbounded color palette and aim to use the smallest
colors and - as a consequence - the smallest number of colors. We identify classes
of bipartite graphs where reoptimization is as hard as or even harder than optimization
from scratch, i. e. starting with a random initialization. Even adding a single
edge can lead to hard symmetry problems. However, graph classes that are hard
for one algorithm turn out to be easy for others. In most cases our bounds show
that reoptimization is faster than optimizing from scratch. Furthermore, we show
how to speed up computations by using problem specific operators concentrating
on parts of the graph where changes have occurred.
author:
- first_name: Jakob
full_name: Bossek, Jakob
id: '102979'
last_name: Bossek
orcid: 0000-0002-4121-4668
- first_name: Frank
full_name: Neumann, Frank
last_name: Neumann
- first_name: Pan
full_name: Peng, Pan
last_name: Peng
- first_name: Dirk
full_name: Sudholt, Dirk
last_name: Sudholt
citation:
ama: 'Bossek J, Neumann F, Peng P, Sudholt D. Runtime Analysis of Randomized Search
Heuristics for Dynamic Graph Coloring. In: Proceedings of the Genetic and Evolutionary
Computation Conference. GECCO ’19. Association for Computing Machinery; 2019:1443–1451.
doi:10.1145/3321707.3321792'
apa: Bossek, J., Neumann, F., Peng, P., & Sudholt, D. (2019). Runtime Analysis
of Randomized Search Heuristics for Dynamic Graph Coloring. Proceedings of
the Genetic and Evolutionary Computation Conference, 1443–1451. https://doi.org/10.1145/3321707.3321792
bibtex: '@inproceedings{Bossek_Neumann_Peng_Sudholt_2019, place={New York, NY, USA},
series={GECCO ’19}, title={Runtime Analysis of Randomized Search Heuristics for
Dynamic Graph Coloring}, DOI={10.1145/3321707.3321792},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
publisher={Association for Computing Machinery}, author={Bossek, Jakob and Neumann,
Frank and Peng, Pan and Sudholt, Dirk}, year={2019}, pages={1443–1451}, collection={GECCO
’19} }'
chicago: 'Bossek, Jakob, Frank Neumann, Pan Peng, and Dirk Sudholt. “Runtime Analysis
of Randomized Search Heuristics for Dynamic Graph Coloring.” In Proceedings
of the Genetic and Evolutionary Computation Conference, 1443–1451. GECCO ’19.
New York, NY, USA: Association for Computing Machinery, 2019. https://doi.org/10.1145/3321707.3321792.'
ieee: 'J. Bossek, F. Neumann, P. Peng, and D. Sudholt, “Runtime Analysis of Randomized
Search Heuristics for Dynamic Graph Coloring,” in Proceedings of the Genetic
and Evolutionary Computation Conference, 2019, pp. 1443–1451, doi: 10.1145/3321707.3321792.'
mla: Bossek, Jakob, et al. “Runtime Analysis of Randomized Search Heuristics for
Dynamic Graph Coloring.” Proceedings of the Genetic and Evolutionary Computation
Conference, Association for Computing Machinery, 2019, pp. 1443–1451, doi:10.1145/3321707.3321792.
short: 'J. Bossek, F. Neumann, P. Peng, D. Sudholt, in: Proceedings of the Genetic
and Evolutionary Computation Conference, Association for Computing Machinery,
New York, NY, USA, 2019, pp. 1443–1451.'
date_created: 2023-11-14T15:58:52Z
date_updated: 2023-12-13T10:42:37Z
department:
- _id: '819'
doi: 10.1145/3321707.3321792
extern: '1'
keyword:
- dynamic optimization
- evolutionary algorithms
- running time analysis
- theory
language:
- iso: eng
page: 1443–1451
place: New York, NY, USA
publication: Proceedings of the Genetic and Evolutionary Computation Conference
publication_identifier:
isbn:
- 978-1-4503-6111-8
publication_status: published
publisher: Association for Computing Machinery
series_title: GECCO ’19
status: public
title: Runtime Analysis of Randomized Search Heuristics for Dynamic Graph Coloring
type: conference
user_id: '102979'
year: '2019'
...