Irene Teinemaa
Irene Teinemaa
Machine Learning at Booking.com
Geverifieerd e-mailadres voor booking.com - Homepage
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Clustering-based predictive process monitoring
C Di Francescomarino, M Dumas, FM Maggi, I Teinemaa
IEEE transactions on services computing 12 (6), 896-909, 2016
992016
Outcome-oriented predictive process monitoring: review and benchmark
I Teinemaa, M Dumas, M La Rosa, FM Maggi
ACM Transactions on Knowledge Discovery in Data 13 (2), 17:1-17:57, 2019
792019
Predictive business process monitoring with structured and unstructured data
I Teinemaa, M Dumas, FM Maggi, C Di Francescomarino
International Conference on Business Process Management, 401-417, 2016
592016
Semantics and analysis of DMN decision tables
D Calvanese, M Dumas, Ü Laurson, FM Maggi, M Montali, I Teinemaa
International Conference on Business Process Management, 217-233, 2016
472016
Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring
I Verenich, M Dumas, ML Rosa, FM Maggi, I Teinemaa
ACM Transactions on Intelligent Systems and Technology (TIST) 10 (4), 1-34, 2019
432019
Semantics, analysis and simplification of DMN decision tables
D Calvanese, M Dumas, Ü Laurson, FM Maggi, M Montali, I Teinemaa
Information Systems 78, 112-125, 2018
252018
Temporal Stability in Predictive Process Monitoring
I Teinemaa, M Dumas, A Leontjeva, FM Maggi
Data Mining and Knowledge Discovery 32 (5), 1306–1338, 2018
182018
An interdisciplinary comparison of sequence modeling methods for next-element prediction
N Tax, I Teinemaa, SJ van Zelst
arXiv preprint arXiv:1811.00062, 2018
132018
Alarm-Based Prescriptive Process Monitoring
I Teinemaa, N Tax, M de Leoni, M Dumas, FM Maggi
International Conference on Business Process Management 329, 91-107, 2018
132018
BPIC 2015: Diagnostics of building permit application process in dutch municipalities
I Teinemaa, A Leontjeva, KO Masing
BPI Challenge Report 72, 2015
112015
An interdisciplinary comparison of sequence modeling methods for next-element prediction
N Tax, I Teinemaa, SJ van Zelst
Software and Systems Modeling 19 (6), 1345-1365, 2020
92020
A ProM Operational Support Provider for Predictive Monitoring of Business Processes.
M Federici, W Rizzi, C Di Francescomarino, M Dumas, C Ghidini, ...
BPM (Demos), 1-5, 2015
72015
Fire now, fire later: alarm-based systems for prescriptive process monitoring
SA Fahrenkrog-Petersen, N Tax, I Teinemaa, M Dumas, M de Leoni, ...
arXiv preprint arXiv:1905.09568, 2019
52019
Community-based prediction of activity change in Skype
I Teinemaa, A Leontjeva, M Dumas, R Kikas
2015 IEEE/ACM International Conference on Advances in Social Networks …, 2015
52015
An experimental evaluation of the generalizing capabilities of process discovery techniques and black-box sequence models
N Tax, SJ van Zelst, I Teinemaa
Enterprise, Business-Process and Information Systems Modeling, 165-180, 2018
42018
Process Mining Meets Causal Machine Learning: Discovering Causal Rules from Event Logs
ZD Bozorgi, I Teinemaa, M Dumas, M La Rosa, A Polyvyanyy
2020 2nd International Conference on Process Mining (ICPM), 129-136, 2020
32020
Personalization in Practice: Methods and Applications
D Goldenberg, K Kofman, J Albert, S Mizrachi, A Horowitz, I Teinemaa
Proceedings of the 14th ACM International Conference on Web Search and Data …, 2021
22021
Predictive and Prescriptive Monitoring of Business Process Outcomes.
I Teinemaa, B Depaire
BPM (PhD/Demos), 15-19, 2019
22019
Automatic Playlist Continuation through a Composition of Collaborative Filters
I Teinemaa, N Tax, C Bentes
arXiv preprint arXiv:1808.04288, 2018
12018
Uplift Modeling: from Causal Inference to Personalization
I Teinemaa, J Albert, D Goldenberg
2021
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Artikelen 1–20