---
_id: '50437'
abstract:
- lang: eng
text: The humanitarian crisis resulting from the Russian invasion of Ukraine has
led to millions of displaced individuals across Europe. Addressing the evolving
needs of these refugees is crucial for hosting countries and humanitarian organizations.
This study leverages social media analytics to supplement traditional surveys,
providing real-time insights into refugee needs by analyzing over two million
messages from Telegram, a vital platform for Ukrainian refugees in Germany. We
employ Natural Language Processing techniques, including language identification,
sentiment analysis, and topic modeling, to identify well-defined topic clusters
such as housing, financial and legal assistance, language courses, job market
access, and medical needs. Our findings also reveal changes in topic occurrence
and nature over time. To support practitioners, we introduce an interactive web-based
dashboard for continuous analysis of refugee needs.
author:
- first_name: Raphael
full_name: Reimann, Raphael
last_name: Reimann
- first_name: Matthew
full_name: Caron, Matthew
id: '60721'
last_name: Caron
citation:
ama: 'Reimann R, Caron M. Analyzing the Needs of Ukrainian Refugees on Telegram
in Real-Time: A Machine Learning Approach. In: Wirtschaftsinformatik. ;
2023.'
apa: 'Reimann, R., & Caron, M. (2023). Analyzing the Needs of Ukrainian Refugees
on Telegram in Real-Time: A Machine Learning Approach. Wirtschaftsinformatik.
Wirtschaftsinformatik, Paderborn, Germany.'
bibtex: '@inproceedings{Reimann_Caron_2023, title={Analyzing the Needs of Ukrainian
Refugees on Telegram in Real-Time: A Machine Learning Approach}, booktitle={Wirtschaftsinformatik},
author={Reimann, Raphael and Caron, Matthew}, year={2023} }'
chicago: 'Reimann, Raphael, and Matthew Caron. “Analyzing the Needs of Ukrainian
Refugees on Telegram in Real-Time: A Machine Learning Approach.” In Wirtschaftsinformatik,
2023.'
ieee: 'R. Reimann and M. Caron, “Analyzing the Needs of Ukrainian Refugees on Telegram
in Real-Time: A Machine Learning Approach,” presented at the Wirtschaftsinformatik,
Paderborn, Germany, 2023.'
mla: 'Reimann, Raphael, and Matthew Caron. “Analyzing the Needs of Ukrainian Refugees
on Telegram in Real-Time: A Machine Learning Approach.” Wirtschaftsinformatik,
2023.'
short: 'R. Reimann, M. Caron, in: Wirtschaftsinformatik, 2023.'
conference:
end_date: 2023-09-21
location: Paderborn, Germany
name: Wirtschaftsinformatik
start_date: 2023-09-18
date_created: 2024-01-10T15:15:19Z
date_updated: 2024-01-10T15:20:13Z
department:
- _id: '196'
language:
- iso: eng
main_file_link:
- url: https://aisel.aisnet.org/wi2023/100/
publication: Wirtschaftsinformatik
publication_status: published
status: public
title: 'Analyzing the Needs of Ukrainian Refugees on Telegram in Real-Time: A Machine
Learning Approach'
type: conference
user_id: '60721'
year: '2023'
...
---
_id: '37058'
abstract:
- lang: eng
text: "Digital technologies have made the line of visibility more transparent, enabling
customers to get deeper insights into an organization’s core operations than ever
before. This creates new challenges for organizations trying to consistently deliver
high-quality customer experiences. In this paper we conduct an empirical analysis
of customers’ preferences and their willingness-to-pay for different degrees of
process transparency, using the example of digitally-enabled business-to-customer
delivery services. Applying conjoint analysis, we quantify customers’ preferences
and willingness-to-pay for different service attributes and levels. Our contributions
are two-fold: For research, we provide empirical measurements of customers’ preferences
and their willingness-to-pay for process transparency, suggesting that more is
not always better. Additionally, we provide a blueprint of how conjoint analysis
can be applied to study design decisions regarding changing an organization’s
digital line of visibility. For practice, our findings enable service managers
to make decisions about process transparency and establishing different levels
of service quality.\r\n"
author:
- first_name: Katharina
full_name: Brennig, Katharina
id: '51905'
last_name: Brennig
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Brennig K, Müller O. More Isn’t Always Better – Measuring Customers’ Preferences
for Digital Process Transparency. In: Hawaii International Conference on System
Sciences. ; 2023.'
apa: Brennig, K., & Müller, O. (2023). More Isn’t Always Better – Measuring
Customers’ Preferences for Digital Process Transparency. Hawaii International
Conference on System Sciences. 56th Hawaii International Conference on System
Sciences, Lāhainā.
bibtex: '@inproceedings{Brennig_Müller_2023, title={More Isn’t Always Better – Measuring
Customers’ Preferences for Digital Process Transparency}, booktitle={Hawaii International
Conference on System Sciences}, author={Brennig, Katharina and Müller, Oliver},
year={2023} }'
chicago: Brennig, Katharina, and Oliver Müller. “More Isn’t Always Better – Measuring
Customers’ Preferences for Digital Process Transparency.” In Hawaii International
Conference on System Sciences, 2023.
ieee: K. Brennig and O. Müller, “More Isn’t Always Better – Measuring Customers’
Preferences for Digital Process Transparency,” presented at the 56th Hawaii International
Conference on System Sciences, Lāhainā, 2023.
mla: Brennig, Katharina, and Oliver Müller. “More Isn’t Always Better – Measuring
Customers’ Preferences for Digital Process Transparency.” Hawaii International
Conference on System Sciences, 2023.
short: 'K. Brennig, O. Müller, in: Hawaii International Conference on System Sciences,
2023.'
conference:
end_date: '20230106'
location: Lāhainā
name: ' 56th Hawaii International Conference on System Sciences'
start_date: '20230103'
date_created: 2023-01-17T11:34:56Z
date_updated: 2024-01-11T11:21:28Z
department:
- _id: '196'
has_accepted_license: '1'
keyword:
- Digital Services
- Line of Visibility
- Process Transparency
- Customer Preferences
- Conjoint Analysis
language:
- iso: eng
publication: Hawaii International Conference on System Sciences
publication_identifier:
unknown:
- 978-0-9981331-6-4
publication_status: published
status: public
title: More Isn’t Always Better – Measuring Customers’ Preferences for Digital Process
Transparency
type: conference
user_id: '51905'
year: '2023'
...
---
_id: '50450'
author:
- first_name: Katharina
full_name: Brennig, Katharina
id: '51905'
last_name: Brennig
- first_name: Kay
full_name: Benkert, Kay
last_name: Benkert
- first_name: Bernd
full_name: Löhr, Bernd
id: '56760'
last_name: Löhr
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Brennig K, Benkert K, Löhr B, Müller O. Text-Aware Predictive Process Monitoring
of Knowledge-Intensive Processes: Does Control Flow Matter? In: Business Process
Management Workshops. ; 2023. doi:10.1007/978-3-031-50974-2_33'
apa: 'Brennig, K., Benkert, K., Löhr, B., & Müller, O. (2023). Text-Aware Predictive
Process Monitoring of Knowledge-Intensive Processes: Does Control Flow Matter?
In Business Process Management Workshops. https://doi.org/10.1007/978-3-031-50974-2_33'
bibtex: '@inbook{Brennig_Benkert_Löhr_Müller_2023, title={Text-Aware Predictive
Process Monitoring of Knowledge-Intensive Processes: Does Control Flow Matter?},
DOI={10.1007/978-3-031-50974-2_33},
booktitle={Business Process Management Workshops}, author={Brennig, Katharina
and Benkert, Kay and Löhr, Bernd and Müller, Oliver}, year={2023} }'
chicago: 'Brennig, Katharina, Kay Benkert, Bernd Löhr, and Oliver Müller. “Text-Aware
Predictive Process Monitoring of Knowledge-Intensive Processes: Does Control Flow
Matter?” In Business Process Management Workshops, 2023. https://doi.org/10.1007/978-3-031-50974-2_33.'
ieee: 'K. Brennig, K. Benkert, B. Löhr, and O. Müller, “Text-Aware Predictive Process
Monitoring of Knowledge-Intensive Processes: Does Control Flow Matter?,” in Business
Process Management Workshops, 2023.'
mla: 'Brennig, Katharina, et al. “Text-Aware Predictive Process Monitoring of Knowledge-Intensive
Processes: Does Control Flow Matter?” Business Process Management Workshops,
2023, doi:10.1007/978-3-031-50974-2_33.'
short: 'K. Brennig, K. Benkert, B. Löhr, O. Müller, in: Business Process Management
Workshops, 2023.'
date_created: 2024-01-11T09:26:05Z
date_updated: 2024-01-11T11:22:35Z
department:
- _id: '196'
doi: 10.1007/978-3-031-50974-2_33
language:
- iso: eng
publication: Business Process Management Workshops
publication_identifier:
isbn:
- '9783031509735'
- '9783031509742'
issn:
- 1865-1348
- 1865-1356
publication_status: published
status: public
title: 'Text-Aware Predictive Process Monitoring of Knowledge-Intensive Processes:
Does Control Flow Matter?'
type: book_chapter
user_id: '51905'
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: '50459'
abstract:
- lang: eng
text: Organizations employ process mining to discover, check, or enhance process
models based on data from information systems to improve business processes. Even
though process mining is increasingly relevant in academia and organizations,
achieving process mining excellence and generating business value through its
application is elusive. Maturity models can help to manage interdisciplinary teams
in their efforts to plan, implement, and manage process mining in organizations.
However, while numerous maturity models on business process management (BPM) are
available, recent calls for process mining maturity models indicate a gap in the
current knowledge base. We systematically design and develop a comprehensive process
mining maturity model that consists of five factors comprising 23 elements, which
organizations need to develop to apply process mining sustainably and successfully.
We contribute to the knowledge base by the exaptation of existing BPM maturity
models, and validate our model through its application to a real-world scenario.
author:
- first_name: Jonathan
full_name: Brock, Jonathan
last_name: Brock
- first_name: Bernd
full_name: Löhr, Bernd
id: '56760'
last_name: Löhr
orcid: 0000-0001-9581-4602
- first_name: Katharina
full_name: Brennig, Katharina
id: '51905'
last_name: Brennig
- first_name: Thilo
full_name: Seger, Thilo
last_name: Seger
- first_name: Christian
full_name: Bartelheimer, Christian
id: '49160'
last_name: Bartelheimer
- first_name: Sebastian
full_name: von Enzberg, Sebastian
last_name: von Enzberg
- first_name: Arno
full_name: Kühn, Arno
last_name: Kühn
- first_name: Roman
full_name: Dumitrescu, Roman
last_name: Dumitrescu
citation:
ama: 'Brock J, Löhr B, Brennig K, et al. A Process Mining Maturity Model: Enabling
Organizations to Assess and Improve their Process Mining Activities. In: European
Conference on Information Systems (ECIS). ; 2023.'
apa: 'Brock, J., Löhr, B., Brennig, K., Seger, T., Bartelheimer, C., von Enzberg,
S., Kühn, A., & Dumitrescu, R. (2023). A Process Mining Maturity Model: Enabling
Organizations to Assess and Improve their Process Mining Activities. European
Conference on Information Systems (ECIS).'
bibtex: '@inproceedings{Brock_Löhr_Brennig_Seger_Bartelheimer_von Enzberg_Kühn_Dumitrescu_2023,
title={A Process Mining Maturity Model: Enabling Organizations to Assess and Improve
their Process Mining Activities}, booktitle={European Conference on Information
Systems (ECIS)}, author={Brock, Jonathan and Löhr, Bernd and Brennig, Katharina
and Seger, Thilo and Bartelheimer, Christian and von Enzberg, Sebastian and Kühn,
Arno and Dumitrescu, Roman}, year={2023} }'
chicago: 'Brock, Jonathan, Bernd Löhr, Katharina Brennig, Thilo Seger, Christian
Bartelheimer, Sebastian von Enzberg, Arno Kühn, and Roman Dumitrescu. “A Process
Mining Maturity Model: Enabling Organizations to Assess and Improve Their Process
Mining Activities.” In European Conference on Information Systems (ECIS),
2023.'
ieee: 'J. Brock et al., “A Process Mining Maturity Model: Enabling Organizations
to Assess and Improve their Process Mining Activities,” 2023.'
mla: 'Brock, Jonathan, et al. “A Process Mining Maturity Model: Enabling Organizations
to Assess and Improve Their Process Mining Activities.” European Conference
on Information Systems (ECIS), 2023.'
short: 'J. Brock, B. Löhr, K. Brennig, T. Seger, C. Bartelheimer, S. von Enzberg,
A. Kühn, R. Dumitrescu, in: European Conference on Information Systems (ECIS),
2023.'
date_created: 2024-01-11T11:32:42Z
date_updated: 2025-05-21T08:42:16Z
department:
- _id: '196'
language:
- iso: eng
main_file_link:
- url: https://aisel.aisnet.org/ecis2023_rp/256/
publication: European Conference on Information Systems (ECIS)
status: public
title: 'A Process Mining Maturity Model: Enabling Organizations to Assess and Improve
their Process Mining Activities'
type: conference
user_id: '56760'
year: '2023'
...
---
_id: '45112'
article_type: letter_note
author:
- first_name: Daniel
full_name: Beverungen, Daniel
id: '59677'
last_name: Beverungen
- first_name: Dennis
full_name: Kundisch, Dennis
id: '21117'
last_name: Kundisch
- first_name: Milad
full_name: Mirbabaie, Milad
id: '88691'
last_name: Mirbabaie
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
- first_name: Guido
full_name: Schryen, Guido
id: '72850'
last_name: Schryen
- first_name: Simon Thanh-Nam
full_name: Trang, Simon Thanh-Nam
id: '98948'
last_name: Trang
orcid: 0000-0002-4784-4038
- first_name: Matthias
full_name: Trier, Matthias
id: '72744'
last_name: Trier
citation:
ama: Beverungen D, Kundisch D, Mirbabaie M, et al. Digital Responsibility – a Multilevel
Framework for Responsible Digitalization. Business & Information Systems
Engineering. 2023;65(4):463-474. doi:10.1007/s12599-023-00822-x
apa: Beverungen, D., Kundisch, D., Mirbabaie, M., Müller, O., Schryen, G., Trang,
S. T.-N., & Trier, M. (2023). Digital Responsibility – a Multilevel Framework
for Responsible Digitalization. Business & Information Systems Engineering,
65(4), 463–474. https://doi.org/10.1007/s12599-023-00822-x
bibtex: '@article{Beverungen_Kundisch_Mirbabaie_Müller_Schryen_Trang_Trier_2023,
title={Digital Responsibility – a Multilevel Framework for Responsible Digitalization},
volume={65}, DOI={10.1007/s12599-023-00822-x},
number={4}, journal={Business & Information Systems Engineering}, author={Beverungen,
Daniel and Kundisch, Dennis and Mirbabaie, Milad and Müller, Oliver and Schryen,
Guido and Trang, Simon Thanh-Nam and Trier, Matthias}, year={2023}, pages={463–474}
}'
chicago: 'Beverungen, Daniel, Dennis Kundisch, Milad Mirbabaie, Oliver Müller, Guido
Schryen, Simon Thanh-Nam Trang, and Matthias Trier. “Digital Responsibility –
a Multilevel Framework for Responsible Digitalization.” Business & Information
Systems Engineering 65, no. 4 (2023): 463–74. https://doi.org/10.1007/s12599-023-00822-x.'
ieee: 'D. Beverungen et al., “Digital Responsibility – a Multilevel Framework
for Responsible Digitalization,” Business & Information Systems Engineering,
vol. 65, no. 4, pp. 463–474, 2023, doi: 10.1007/s12599-023-00822-x.'
mla: Beverungen, Daniel, et al. “Digital Responsibility – a Multilevel Framework
for Responsible Digitalization.” Business & Information Systems Engineering,
vol. 65, no. 4, 2023, pp. 463–74, doi:10.1007/s12599-023-00822-x.
short: D. Beverungen, D. Kundisch, M. Mirbabaie, O. Müller, G. Schryen, S.T.-N.
Trang, M. Trier, Business & Information Systems Engineering 65 (2023) 463–474.
date_created: 2023-05-19T07:21:29Z
date_updated: 2026-03-12T13:44:38Z
ddc:
- '000'
department:
- _id: '277'
- _id: '196'
- _id: '646'
- _id: '526'
- _id: '198'
- _id: '792'
- _id: '276'
- _id: '681'
doi: 10.1007/s12599-023-00822-x
file:
- access_level: closed
content_type: application/pdf
creator: schryen
date_created: 2023-07-06T13:02:00Z
date_updated: 2023-07-06T13:02:00Z
file_id: '45871'
file_name: Digital_Responsibility- A Multilevel Framework for Responsible Digitilization-
BISE Springer VERSION.pdf
file_size: 373767
relation: main_file
success: 1
file_date_updated: 2023-07-06T13:02:00Z
has_accepted_license: '1'
intvolume: ' 65'
issue: '4'
language:
- iso: eng
page: 463 - 474
publication: Business & Information Systems Engineering
publication_status: published
status: public
title: Digital Responsibility – a Multilevel Framework for Responsible Digitalization
type: journal_article
user_id: '16205'
volume: 65
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: '29539'
abstract:
- lang: eng
text: Explainable Artificial Intelligence (XAI) is currently an important topic
for the application of Machine Learning (ML) in high-stakes decision scenarios.
Related research focuses on evaluating ML algorithms in terms of interpretability.
However, providing a human understandable explanation of an intelligent system
does not only relate to the used ML algorithm. The data and features used also
have a considerable impact on interpretability. In this paper, we develop a taxonomy
for describing XAI systems based on aspects about the algorithm and data. The
proposed taxonomy gives researchers and practitioners opportunities to describe
and evaluate current XAI systems with respect to interpretability and guides the
future development of this class of systems.
author:
- first_name: Jan-Peter
full_name: Kucklick, Jan-Peter
id: '77066'
last_name: Kucklick
citation:
ama: 'Kucklick J-P. Towards a model- and data-focused taxonomy of XAI systems. In:
Wirtschaftsinformatik 2022 Proceedings. ; 2022.'
apa: Kucklick, J.-P. (2022). Towards a model- and data-focused taxonomy of XAI systems.
Wirtschaftsinformatik 2022 Proceedings. Wirtschaftsinformatik 2022 (WI22),
Nürnberg (online).
bibtex: '@inproceedings{Kucklick_2022, title={Towards a model- and data-focused
taxonomy of XAI systems}, booktitle={Wirtschaftsinformatik 2022 Proceedings},
author={Kucklick, Jan-Peter}, year={2022} }'
chicago: Kucklick, Jan-Peter. “Towards a Model- and Data-Focused Taxonomy of XAI
Systems.” In Wirtschaftsinformatik 2022 Proceedings, 2022.
ieee: J.-P. Kucklick, “Towards a model- and data-focused taxonomy of XAI systems,”
presented at the Wirtschaftsinformatik 2022 (WI22), Nürnberg (online), 2022.
mla: Kucklick, Jan-Peter. “Towards a Model- and Data-Focused Taxonomy of XAI Systems.”
Wirtschaftsinformatik 2022 Proceedings, 2022.
short: 'J.-P. Kucklick, in: Wirtschaftsinformatik 2022 Proceedings, 2022.'
conference:
end_date: 2022-02-23
location: Nürnberg (online)
name: Wirtschaftsinformatik 2022 (WI22)
start_date: 2022-02-21
date_created: 2022-01-26T08:22:03Z
date_updated: 2022-01-26T08:24:30Z
department:
- _id: '195'
- _id: '196'
keyword:
- Explainable Artificial Intelligence
- XAI
- Interpretability
- Decision Support Systems
- Taxonomy
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1056&context=wi2022
oa: '1'
publication: Wirtschaftsinformatik 2022 Proceedings
status: public
title: Towards a model- and data-focused taxonomy of XAI systems
type: conference
user_id: '77066'
year: '2022'
...
---
_id: '32866'
article_number: '101734'
author:
- first_name: Arisa
full_name: Shollo, Arisa
last_name: Shollo
- first_name: Konstantin
full_name: Hopf, Konstantin
last_name: Hopf
- first_name: Tiemo
full_name: Thiess, Tiemo
last_name: Thiess
- first_name: Oliver
full_name: Müller, Oliver
last_name: Müller
citation:
ama: 'Shollo A, Hopf K, Thiess T, Müller O. Shifting ML value creation mechanisms:
A process model of ML value creation. The Journal of Strategic Information
Systems. 2022;31(3). doi:10.1016/j.jsis.2022.101734'
apa: 'Shollo, A., Hopf, K., Thiess, T., & Müller, O. (2022). Shifting ML value
creation mechanisms: A process model of ML value creation. The Journal of Strategic
Information Systems, 31(3), Article 101734. https://doi.org/10.1016/j.jsis.2022.101734'
bibtex: '@article{Shollo_Hopf_Thiess_Müller_2022, title={Shifting ML value creation
mechanisms: A process model of ML value creation}, volume={31}, DOI={10.1016/j.jsis.2022.101734},
number={3101734}, journal={The Journal of Strategic Information Systems}, publisher={Elsevier
BV}, author={Shollo, Arisa and Hopf, Konstantin and Thiess, Tiemo and Müller,
Oliver}, year={2022} }'
chicago: 'Shollo, Arisa, Konstantin Hopf, Tiemo Thiess, and Oliver Müller. “Shifting
ML Value Creation Mechanisms: A Process Model of ML Value Creation.” The Journal
of Strategic Information Systems 31, no. 3 (2022). https://doi.org/10.1016/j.jsis.2022.101734.'
ieee: 'A. Shollo, K. Hopf, T. Thiess, and O. Müller, “Shifting ML value creation
mechanisms: A process model of ML value creation,” The Journal of Strategic
Information Systems, vol. 31, no. 3, Art. no. 101734, 2022, doi: 10.1016/j.jsis.2022.101734.'
mla: 'Shollo, Arisa, et al. “Shifting ML Value Creation Mechanisms: A Process Model
of ML Value Creation.” The Journal of Strategic Information Systems, vol.
31, no. 3, 101734, Elsevier BV, 2022, doi:10.1016/j.jsis.2022.101734.'
short: A. Shollo, K. Hopf, T. Thiess, O. Müller, The Journal of Strategic Information
Systems 31 (2022).
date_created: 2022-08-17T07:03:55Z
date_updated: 2022-08-17T07:16:12Z
ddc:
- '000'
department:
- _id: '196'
doi: 10.1016/j.jsis.2022.101734
file:
- access_level: closed
content_type: application/pdf
creator: omueller
date_created: 2022-08-17T07:04:41Z
date_updated: 2022-08-17T07:04:41Z
file_id: '32867'
file_name: 1-s2.0-S0963868722000300-main.pdf
file_size: 1980258
relation: main_file
success: 1
file_date_updated: 2022-08-17T07:04:41Z
has_accepted_license: '1'
intvolume: ' 31'
issue: '3'
keyword:
- Information Systems and Management
- Information Systems
- Management Information Systems
language:
- iso: eng
publication: The Journal of Strategic Information Systems
publication_identifier:
issn:
- 0963-8687
publication_status: published
publisher: Elsevier BV
status: public
title: 'Shifting ML value creation mechanisms: A process model of ML value creation'
type: journal_article
user_id: '72849'
volume: 31
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: '36912'
abstract:
- lang: eng
text: Existing process mining methods are primarily designed for processes that
have reached a high degree of digitalization and standardization. In contrast,
the literature has only begun to discuss how process mining can be applied to
knowledge-intensive processes—such as product innovation processes—that involve
creative activities, require organizational flexibility, depend on single actors’
decision autonomy, and target process-external goals such as customer satisfaction.
Due to these differences, existing Process Mining methods cannot be applied out-of-the-box
to analyze knowledge-intensive processes. In this paper, we employ Action Design
Research (ADR) to design and evaluate a process mining approach for knowledge-intensive
processes. More specifically, we draw on the two processes of product innovation
and engineer-to-order in manufacturing contexts. We collected data from 27 interviews
and conducted 49 workshops to evaluate our IT artifact at different stages in
the ADR process. From a theoretical perspective, we contribute five design principles
and a conceptual artifact that prescribe how process mining ought to be designed
for knowledge-intensive processes in manufacturing. From a managerial perspective,
we demonstrate how enacting these principles enables their application in practice.
author:
- first_name: Bernd
full_name: Löhr, Bernd
id: '56760'
last_name: Löhr
- first_name: Katharina
full_name: Brennig, Katharina
id: '51905'
last_name: Brennig
- first_name: Christian
full_name: Bartelheimer, Christian
id: '49160'
last_name: Bartelheimer
- first_name: Daniel
full_name: Beverungen, Daniel
id: '59677'
last_name: Beverungen
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Löhr B, Brennig K, Bartelheimer C, Beverungen D, Müller O. Process Mining
of Knowledge-Intensive Processes: An Action Design Research Study in Manufacturing.
In: International Conference on Business Process Management. ; 2022. doi:10.1007/978-3-031-16103-2_18'
apa: 'Löhr, B., Brennig, K., Bartelheimer, C., Beverungen, D., & Müller, O.
(2022). Process Mining of Knowledge-Intensive Processes: An Action Design Research
Study in Manufacturing. International Conference on Business Process Management.
https://doi.org/10.1007/978-3-031-16103-2_18'
bibtex: '@inproceedings{Löhr_Brennig_Bartelheimer_Beverungen_Müller_2022, title={Process
Mining of Knowledge-Intensive Processes: An Action Design Research Study in Manufacturing},
DOI={10.1007/978-3-031-16103-2_18},
booktitle={International Conference on Business Process Management}, author={Löhr,
Bernd and Brennig, Katharina and Bartelheimer, Christian and Beverungen, Daniel
and Müller, Oliver}, year={2022} }'
chicago: 'Löhr, Bernd, Katharina Brennig, Christian Bartelheimer, Daniel Beverungen,
and Oliver Müller. “Process Mining of Knowledge-Intensive Processes: An Action
Design Research Study in Manufacturing.” In International Conference on Business
Process Management, 2022. https://doi.org/10.1007/978-3-031-16103-2_18.'
ieee: 'B. Löhr, K. Brennig, C. Bartelheimer, D. Beverungen, and O. Müller, “Process
Mining of Knowledge-Intensive Processes: An Action Design Research Study in Manufacturing,”
2022, doi: 10.1007/978-3-031-16103-2_18.'
mla: 'Löhr, Bernd, et al. “Process Mining of Knowledge-Intensive Processes: An Action
Design Research Study in Manufacturing.” International Conference on Business
Process Management, 2022, doi:10.1007/978-3-031-16103-2_18.'
short: 'B. Löhr, K. Brennig, C. Bartelheimer, D. Beverungen, O. Müller, in: International
Conference on Business Process Management, 2022.'
date_created: 2023-01-16T11:04:54Z
date_updated: 2024-01-11T11:35:54Z
department:
- _id: '196'
doi: 10.1007/978-3-031-16103-2_18
language:
- iso: eng
publication: International Conference on Business Process Management
publication_identifier:
isbn:
- 978-3-031-16103-2
status: public
title: 'Process Mining of Knowledge-Intensive Processes: An Action Design Research
Study in Manufacturing'
type: conference
user_id: '51905'
year: '2022'
...
---
_id: '42631'
abstract:
- lang: eng
text: In recent years, many cases of deep neural networks failing dramatically when
faced with adversarial or real-world examples have been reported. Such failures,
which are quite hard to detect, are often related to a generalization problem
known as shortcut learning. Yet, with state-of-the-art transformer models now
being ubiquitous in financial text mining, one cannot help but wonder how reliable
the results conveyed in the ever-growing literature genuinely are. Against this
background, we expose, in this work, how vulnerable contemporary financial text
mining approaches are to shortcut learning. Focussing on the common learning task
of financial sentiment classification, we assess, using two entity-based sampling
strategies and our publicly-available dataset, the discrepancies between i.i.d.
and o.o.d. performance estimates of four transformer models. Our results reveal
that o.o.d. performance estimates are consistently weaker than those of their
i.i.d. counterparts, with the error rate increasing by as much as 29.7%, thus,
demonstrating how this issue can, when overlooked, lead to misleading evaluations.
Moreover, we show how additional preprocessing steps, such as entity removal and
vocabulary filtering, can help reduce the effects of shortcut learning by filtering
out entity-related linguistic cues.
author:
- first_name: Matthew
full_name: Caron, Matthew
id: '60721'
last_name: Caron
citation:
ama: 'Caron M. Shortcut Learning in Financial Text Mining: Exposing the Overly Optimistic
Performance Estimates of Text Classification Models under Distribution Shift.
In: 2022 IEEE International Conference on Big Data (Big Data). IEEE; 2022.
doi:10.1109/bigdata55660.2022.10020933'
apa: 'Caron, M. (2022). Shortcut Learning in Financial Text Mining: Exposing the
Overly Optimistic Performance Estimates of Text Classification Models under Distribution
Shift. 2022 IEEE International Conference on Big Data (Big Data). 2022
IEEE International Conference on Big Data (Big Data), Osaka, Japan. https://doi.org/10.1109/bigdata55660.2022.10020933'
bibtex: '@inproceedings{Caron_2022, title={Shortcut Learning in Financial Text Mining:
Exposing the Overly Optimistic Performance Estimates of Text Classification Models
under Distribution Shift}, DOI={10.1109/bigdata55660.2022.10020933},
booktitle={2022 IEEE International Conference on Big Data (Big Data)}, publisher={IEEE},
author={Caron, Matthew}, year={2022} }'
chicago: 'Caron, Matthew. “Shortcut Learning in Financial Text Mining: Exposing
the Overly Optimistic Performance Estimates of Text Classification Models under
Distribution Shift.” In 2022 IEEE International Conference on Big Data (Big
Data). IEEE, 2022. https://doi.org/10.1109/bigdata55660.2022.10020933.'
ieee: 'M. Caron, “Shortcut Learning in Financial Text Mining: Exposing the Overly
Optimistic Performance Estimates of Text Classification Models under Distribution
Shift,” presented at the 2022 IEEE International Conference on Big Data (Big Data),
Osaka, Japan, 2022, doi: 10.1109/bigdata55660.2022.10020933.'
mla: 'Caron, Matthew. “Shortcut Learning in Financial Text Mining: Exposing the
Overly Optimistic Performance Estimates of Text Classification Models under Distribution
Shift.” 2022 IEEE International Conference on Big Data (Big Data), IEEE,
2022, doi:10.1109/bigdata55660.2022.10020933.'
short: 'M. Caron, in: 2022 IEEE International Conference on Big Data (Big Data),
IEEE, 2022.'
conference:
end_date: 2022-12-20
location: Osaka, Japan
name: 2022 IEEE International Conference on Big Data (Big Data)
start_date: 2022-12-17
date_created: 2023-02-28T08:29:30Z
date_updated: 2024-01-15T12:32:06Z
department:
- _id: '196'
doi: 10.1109/bigdata55660.2022.10020933
language:
- iso: eng
main_file_link:
- url: https://ieeexplore.ieee.org/document/10020933
publication: 2022 IEEE International Conference on Big Data (Big Data)
publication_identifier:
eisbn:
- 978-1-6654-8045-1
publication_status: published
publisher: IEEE
status: public
title: 'Shortcut Learning in Financial Text Mining: Exposing the Overly Optimistic
Performance Estimates of Text Classification Models under Distribution Shift'
type: conference
user_id: '60721'
year: '2022'
...
---
_id: '25113'
abstract:
- lang: eng
text: Our world is more connected than ever before. Sadly, however, this highly
connected world has made it easier to bully, insult, and propagate hate speech
on the cyberspace. Even though researchers and companies alike have started investigating
this real-world problem, the question remains as to why users are increasingly
being exposed to hate and discrimination online. In fact, the noticeable and persistent
increase in harmful language on social media platforms indicates that the situation
is, actually, only getting worse. Hence, in this work, we show that contemporary
ML methods can help tackle this challenge in an accurate and cost-effective manner.
Our experiments demonstrate that a universal approach combining transfer learning
methods and state-of-the-art Transformer architectures can trigger the efficient
development of toxic language detection models. Consequently, with this universal
approach, we provide platform providers with a simplistic approach capable of
enabling the automated moderation of user-generated content, and as a result,
hope to contribute to making the web a safer place.
author:
- first_name: Matthew
full_name: Caron, Matthew
id: '60721'
last_name: Caron
- first_name: Frederik S.
full_name: Bäumer, Frederik S.
last_name: Bäumer
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Caron M, Bäumer FS, Müller O. Towards Automated Moderation: Enabling Toxic
Language Detection with Transfer Learning and Attention-Based Models. In: 55th
Hawaii International Conference on System Sciences (HICSS). ; 2022.'
apa: 'Caron, M., Bäumer, F. S., & Müller, O. (2022). Towards Automated Moderation:
Enabling Toxic Language Detection with Transfer Learning and Attention-Based Models.
55th Hawaii International Conference on System Sciences (HICSS). 55th Hawaii
International Conference on System Sciences (HICSS), Online.'
bibtex: '@inproceedings{Caron_Bäumer_Müller_2022, title={Towards Automated Moderation:
Enabling Toxic Language Detection with Transfer Learning and Attention-Based Models},
booktitle={55th Hawaii International Conference on System Sciences (HICSS)}, author={Caron,
Matthew and Bäumer, Frederik S. and Müller, Oliver}, year={2022} }'
chicago: 'Caron, Matthew, Frederik S. Bäumer, and Oliver Müller. “Towards Automated
Moderation: Enabling Toxic Language Detection with Transfer Learning and Attention-Based
Models.” In 55th Hawaii International Conference on System Sciences (HICSS),
2022.'
ieee: 'M. Caron, F. S. Bäumer, and O. Müller, “Towards Automated Moderation: Enabling
Toxic Language Detection with Transfer Learning and Attention-Based Models,” presented
at the 55th Hawaii International Conference on System Sciences (HICSS), Online,
2022.'
mla: 'Caron, Matthew, et al. “Towards Automated Moderation: Enabling Toxic Language
Detection with Transfer Learning and Attention-Based Models.” 55th Hawaii International
Conference on System Sciences (HICSS), 2022.'
short: 'M. Caron, F.S. Bäumer, O. Müller, in: 55th Hawaii International Conference
on System Sciences (HICSS), 2022.'
conference:
end_date: 2022-01-07
location: Online
name: 55th Hawaii International Conference on System Sciences (HICSS)
start_date: 2022-01-03
date_created: 2021-09-29T10:06:24Z
date_updated: 2024-01-15T12:37:10Z
department:
- _id: '196'
language:
- iso: eng
main_file_link:
- url: http://hdl.handle.net/10125/79428
publication: 55th Hawaii International Conference on System Sciences (HICSS)
publication_status: published
status: public
title: 'Towards Automated Moderation: Enabling Toxic Language Detection with Transfer
Learning and Attention-Based Models'
type: conference
user_id: '60721'
year: '2022'
...
---
_id: '21204'
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. A Comparison of Multi-View Learning Strategies for
Satellite Image-based Real Estate Appraisal. In: The AAAI-21 Workshop on Knowledge
Discovery from Unstructured Data in Financial Services. ; 2021.'
apa: Kucklick, J.-P., & Müller, O. (2021). A Comparison of Multi-View Learning
Strategies for Satellite Image-based Real Estate Appraisal. In The AAAI-21
Workshop on Knowledge Discovery from Unstructured Data in Financial Services.
bibtex: '@inproceedings{Kucklick_Müller_2021, title={A Comparison of Multi-View
Learning Strategies for Satellite Image-based Real Estate Appraisal}, booktitle={
The AAAI-21 Workshop on Knowledge Discovery from Unstructured Data in Financial
Services}, author={Kucklick, Jan-Peter and Müller, Oliver}, year={2021} }'
chicago: Kucklick, Jan-Peter, and Oliver Müller. “A Comparison of Multi-View Learning
Strategies for Satellite Image-Based Real Estate Appraisal.” In The AAAI-21
Workshop on Knowledge Discovery from Unstructured Data in Financial Services,
2021.
ieee: J.-P. Kucklick and O. Müller, “A Comparison of Multi-View Learning Strategies
for Satellite Image-based Real Estate Appraisal,” in The AAAI-21 Workshop
on Knowledge Discovery from Unstructured Data in Financial Services, 2021.
mla: Kucklick, Jan-Peter, and Oliver Müller. “A Comparison of Multi-View Learning
Strategies for Satellite Image-Based Real Estate Appraisal.” The AAAI-21 Workshop
on Knowledge Discovery from Unstructured Data in Financial Services, 2021.
short: 'J.-P. Kucklick, O. Müller, in: The AAAI-21 Workshop on Knowledge Discovery
from Unstructured Data in Financial Services, 2021.'
conference:
name: The Thirty-Fifth AAAI Conference on Artificial Intelligence
date_created: 2021-02-10T10:05:32Z
date_updated: 2022-01-06T06:54:49Z
department:
- _id: '196'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://aaai-kdf.github.io/kdf2021/assets/pdfs/KDF_21_paper_12.pdf
oa: '1'
publication: ' The AAAI-21 Workshop on Knowledge Discovery from Unstructured Data
in Financial Services'
status: public
title: A Comparison of Multi-View Learning Strategies for Satellite Image-based Real
Estate Appraisal
type: conference
user_id: '71922'
year: '2021'
...
---
_id: '22514'
author:
- first_name: Jan-Peter
full_name: Kucklick, Jan-Peter
id: '77066'
last_name: Kucklick
- first_name: Jennifer
full_name: Müller, Jennifer
id: '82872'
last_name: Müller
- first_name: Daniel
full_name: Beverungen, Daniel
id: '59677'
last_name: Beverungen
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Kucklick J-P, Müller J, Beverungen D, Müller O. Quantifying the Impact of
Location Data for Real Estate Appraisal – A GIS-based Deep Learning Approach.
In: European Conference on Information Systems. ; 2021.'
apa: Kucklick, J.-P., Müller, J., Beverungen, D., & Müller, O. (2021). Quantifying
the Impact of Location Data for Real Estate Appraisal – A GIS-based Deep Learning
Approach. In European Conference on Information Systems. Virtual.
bibtex: '@inproceedings{Kucklick_Müller_Beverungen_Müller_2021, title={Quantifying
the Impact of Location Data for Real Estate Appraisal – A GIS-based Deep Learning
Approach}, booktitle={European Conference on Information Systems}, author={Kucklick,
Jan-Peter and Müller, Jennifer and Beverungen, Daniel and Müller, Oliver}, year={2021}
}'
chicago: Kucklick, Jan-Peter, Jennifer Müller, Daniel Beverungen, and Oliver Müller.
“Quantifying the Impact of Location Data for Real Estate Appraisal – A GIS-Based
Deep Learning Approach.” In European Conference on Information Systems,
2021.
ieee: J.-P. Kucklick, J. Müller, D. Beverungen, and O. Müller, “Quantifying the
Impact of Location Data for Real Estate Appraisal – A GIS-based Deep Learning
Approach,” in European Conference on Information Systems, Virtual, 2021.
mla: Kucklick, Jan-Peter, et al. “Quantifying the Impact of Location Data for Real
Estate Appraisal – A GIS-Based Deep Learning Approach.” European Conference
on Information Systems, 2021.
short: 'J.-P. Kucklick, J. Müller, D. Beverungen, O. Müller, in: European Conference
on Information Systems, 2021.'
conference:
end_date: 2021-06-16
location: Virtual
name: ECIS 2021 - 29th European Conference on Information System
start_date: 2021-06-14
date_created: 2021-06-28T11:30:02Z
date_updated: 2022-01-06T06:55:35Z
department:
- _id: '196'
- _id: '526'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1022&context=ecis2021_rip
oa: '1'
publication: European Conference on Information Systems
status: public
title: Quantifying the Impact of Location Data for Real Estate Appraisal – A GIS-based
Deep Learning Approach
type: conference
user_id: '71922'
year: '2021'
...
---
_id: '32868'
author:
- first_name: Per Rådberg
full_name: Nagbøl, Per Rådberg
last_name: Nagbøl
- first_name: Oliver
full_name: Müller, Oliver
last_name: Müller
- first_name: Oliver
full_name: Krancher, Oliver
last_name: Krancher
citation:
ama: 'Nagbøl PR, Müller O, Krancher O. Designing a Risk Assessment Tool for Artificial
Intelligence Systems. In: The Next Wave of Sociotechnical Design. Springer
International Publishing; 2021. doi:10.1007/978-3-030-82405-1_32'
apa: Nagbøl, P. R., Müller, O., & Krancher, O. (2021). Designing a Risk Assessment
Tool for Artificial Intelligence Systems. In The Next Wave of Sociotechnical
Design. Springer International Publishing. https://doi.org/10.1007/978-3-030-82405-1_32
bibtex: '@inbook{Nagbøl_Müller_Krancher_2021, place={Cham}, title={Designing a Risk
Assessment Tool for Artificial Intelligence Systems}, DOI={10.1007/978-3-030-82405-1_32},
booktitle={The Next Wave of Sociotechnical Design}, publisher={Springer International
Publishing}, author={Nagbøl, Per Rådberg and Müller, Oliver and Krancher, Oliver},
year={2021} }'
chicago: 'Nagbøl, Per Rådberg, Oliver Müller, and Oliver Krancher. “Designing a
Risk Assessment Tool for Artificial Intelligence Systems.” In The Next Wave
of Sociotechnical Design. Cham: Springer International Publishing, 2021. https://doi.org/10.1007/978-3-030-82405-1_32.'
ieee: 'P. R. Nagbøl, O. Müller, and O. Krancher, “Designing a Risk Assessment Tool
for Artificial Intelligence Systems,” in The Next Wave of Sociotechnical Design,
Cham: Springer International Publishing, 2021.'
mla: Nagbøl, Per Rådberg, et al. “Designing a Risk Assessment Tool for Artificial
Intelligence Systems.” The Next Wave of Sociotechnical Design, Springer
International Publishing, 2021, doi:10.1007/978-3-030-82405-1_32.
short: 'P.R. Nagbøl, O. Müller, O. Krancher, in: The Next Wave of Sociotechnical
Design, Springer International Publishing, Cham, 2021.'
date_created: 2022-08-17T07:14:41Z
date_updated: 2022-08-17T07:16:00Z
department:
- _id: '196'
doi: 10.1007/978-3-030-82405-1_32
language:
- iso: eng
place: Cham
publication: The Next Wave of Sociotechnical Design
publication_identifier:
isbn:
- '9783030824044'
- '9783030824051'
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer International Publishing
status: public
title: Designing a Risk Assessment Tool for Artificial Intelligence Systems
type: book_chapter
user_id: '72849'
year: '2021'
...
---
_id: '26812'
author:
- first_name: Dirk
full_name: Leffrang, Dirk
id: '51271'
last_name: Leffrang
orcid: 0000-0001-9004-2391
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Leffrang D, Müller O. Should I Follow this Model? The Effect of Uncertainty
Visualization on the Acceptance of Time Series Forecasts. In: IEEE Workshop
on TRust and EXpertise in Visual Analytics. ; 2021. doi:10.1109/TREX53765.2021.00009'
apa: Leffrang, D., & Müller, O. (2021). Should I Follow this Model? The Effect
of Uncertainty Visualization on the Acceptance of Time Series Forecasts. IEEE
Workshop on TRust and EXpertise in Visual Analytics. 2021 IEEE Visualization
conference. https://doi.org/10.1109/TREX53765.2021.00009
bibtex: '@inproceedings{Leffrang_Müller_2021, title={Should I Follow this Model?
The Effect of Uncertainty Visualization on the Acceptance of Time Series Forecasts},
DOI={10.1109/TREX53765.2021.00009},
booktitle={IEEE Workshop on TRust and EXpertise in Visual Analytics}, author={Leffrang,
Dirk and Müller, Oliver}, year={2021} }'
chicago: Leffrang, Dirk, and Oliver Müller. “Should I Follow This Model? The Effect
of Uncertainty Visualization on the Acceptance of Time Series Forecasts.” In IEEE
Workshop on TRust and EXpertise in Visual Analytics, 2021. https://doi.org/10.1109/TREX53765.2021.00009.
ieee: 'D. Leffrang and O. Müller, “Should I Follow this Model? The Effect of Uncertainty
Visualization on the Acceptance of Time Series Forecasts,” presented at the 2021
IEEE Visualization conference, 2021, doi: 10.1109/TREX53765.2021.00009.'
mla: Leffrang, Dirk, and Oliver Müller. “Should I Follow This Model? The Effect
of Uncertainty Visualization on the Acceptance of Time Series Forecasts.” IEEE
Workshop on TRust and EXpertise in Visual Analytics, 2021, doi:10.1109/TREX53765.2021.00009.
short: 'D. Leffrang, O. Müller, in: IEEE Workshop on TRust and EXpertise in Visual
Analytics, 2021.'
conference:
end_date: 2021-10-19
name: 2021 IEEE Visualization conference
start_date: 2021-10-24
date_created: 2021-10-25T11:11:39Z
date_updated: 2024-01-10T09:55:48Z
department:
- _id: '196'
doi: 10.1109/TREX53765.2021.00009
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://trexvis.github.io/Workshop2021/papers/Leffrang.pdf
oa: '1'
publication: IEEE Workshop on TRust and EXpertise in Visual Analytics
status: public
title: Should I Follow this Model? The Effect of Uncertainty Visualization on the
Acceptance of Time Series Forecasts
type: conference
user_id: '51271'
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: '25029'
abstract:
- lang: eng
text: In early 2021, the finance world was taken by storm by the dramatic price
surge of the GameStop Corp. stock. This rise is being, at least in part, attributed
to a group of Redditors belonging to the now-famous r/wallstreetbets (WSB) subreddit
group. In this work, we set out to address if user activity on the WSB subreddit
is associated with the trading volume of the GME stock. Leveraging a unique dataset
containing more than 4.9 million WSB posts and comments, we assert that user activity
is associated with the trading volume of the GameStop stock. We further show that
posts have a significantly higher predictive power than comments and are especially
helpful for predicting unusually high trading volume. Lastly, as recent events
have shown, we believe that these findings have implications for retail and institutional
investors, trading platforms, and policymakers, as these can have disruptive potential.
author:
- first_name: Matthew
full_name: Caron, Matthew
id: '60721'
last_name: Caron
- first_name: Maryna
full_name: Gulenko, Maryna
id: '64226'
last_name: Gulenko
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Caron M, Gulenko M, Müller O. To the Moon! Analyzing the Community of “Degenerates”
Engaged in the Surge of the GME Stock. In: 42nd International Conference on
Information Systems (ICIS 2021). ; 2021.'
apa: Caron, M., Gulenko, M., & Müller, O. (2021). To the Moon! Analyzing the
Community of “Degenerates” Engaged in the Surge of the GME Stock. 42nd International
Conference on Information Systems (ICIS 2021). 42nd International Conference
on Information Systems (ICIS 2021), Austin, Texas.
bibtex: '@inproceedings{Caron_Gulenko_Müller_2021, title={To the Moon! Analyzing
the Community of “Degenerates” Engaged in the Surge of the GME Stock}, booktitle={42nd
International Conference on Information Systems (ICIS 2021)}, author={Caron, Matthew
and Gulenko, Maryna and Müller, Oliver}, year={2021} }'
chicago: Caron, Matthew, Maryna Gulenko, and Oliver Müller. “To the Moon! Analyzing
the Community of ‘Degenerates’ Engaged in the Surge of the GME Stock.” In 42nd
International Conference on Information Systems (ICIS 2021), 2021.
ieee: M. Caron, M. Gulenko, and O. Müller, “To the Moon! Analyzing the Community
of ‘Degenerates’ Engaged in the Surge of the GME Stock,” presented at the 42nd
International Conference on Information Systems (ICIS 2021), Austin, Texas, 2021.
mla: Caron, Matthew, et al. “To the Moon! Analyzing the Community of ‘Degenerates’
Engaged in the Surge of the GME Stock.” 42nd International Conference on Information
Systems (ICIS 2021), 2021.
short: 'M. Caron, M. Gulenko, O. Müller, in: 42nd International Conference on Information
Systems (ICIS 2021), 2021.'
conference:
end_date: 2021-12-15
location: Austin, Texas
name: 42nd International Conference on Information Systems (ICIS 2021)
start_date: 2021-12-12
date_created: 2021-09-24T09:51:35Z
date_updated: 2023-02-28T08:58:16Z
department:
- _id: '196'
keyword:
- Retail investors
- GameStop
- Social Networks
- Reddit
- WallStreetBets
language:
- iso: eng
main_file_link:
- url: https://aisel.aisnet.org/icis2021/social_media/social_media/13/
publication: 42nd International Conference on Information Systems (ICIS 2021)
publication_status: published
status: public
title: To the Moon! Analyzing the Community of “Degenerates” Engaged in the Surge
of the GME Stock
type: conference
user_id: '60721'
year: '2021'
...
---
_id: '17348'
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. Location, location, location: Satellite image-based
real-estate appraisal. In: Symposium on Statistical Challenges in Electronic
Commerce Research (SCECR). ; 2020.'
apa: 'Kucklick, J.-P., & Müller, O. (2020). Location, location, location: Satellite
image-based real-estate appraisal. In Symposium on Statistical Challenges
in Electronic Commerce Research (SCECR).'
bibtex: '@inproceedings{Kucklick_Müller_2020, title={Location, location, location:
Satellite image-based real-estate appraisal}, booktitle={Symposium on Statistical
Challenges in Electronic Commerce Research (SCECR)}, author={Kucklick, Jan-Peter
and Müller, Oliver}, year={2020} }'
chicago: 'Kucklick, Jan-Peter, and Oliver Müller. “Location, Location, Location:
Satellite Image-Based Real-Estate Appraisal.” In Symposium on Statistical
Challenges in Electronic Commerce Research (SCECR), 2020.'
ieee: 'J.-P. Kucklick and O. Müller, “Location, location, location: Satellite image-based
real-estate appraisal,” in Symposium on Statistical Challenges in Electronic
Commerce Research (SCECR), 2020.'
mla: 'Kucklick, Jan-Peter, and Oliver Müller. “Location, Location, Location: Satellite
Image-Based Real-Estate Appraisal.” Symposium on Statistical Challenges in
Electronic Commerce Research (SCECR), 2020.'
short: 'J.-P. Kucklick, O. Müller, in: Symposium on Statistical Challenges in Electronic
Commerce Research (SCECR), 2020.'
conference:
name: Symposium on Statistical Challenges in Electronic Commerce Research (SCECR)
date_created: 2020-06-27T12:41:10Z
date_updated: 2022-01-06T06:53:08Z
department:
- _id: '196'
external_id:
arxiv:
- '2006.11406'
language:
- iso: eng
publication: Symposium on Statistical Challenges in Electronic Commerce Research (SCECR)
status: public
title: 'Location, location, location: Satellite image-based real-estate appraisal'
type: conference
user_id: '71922'
year: '2020'
...