Zobrazeno 1 - 10 of 64 pro vyhledávání: '"Tax, N."'
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Heuristic mining approaches for high-utility local process models
Autor: Dalmas, Benjamin, Tax, N., Norre, Sylvie, Koutny, M., Kristensen, L.M., Penczek, W.
Publikováno v: Transactions on Petri nets and other models of concurrency XIII, 27-51
STARTPAGE=27;ENDPAGE=51;TITLE=Transactions on Petri nets and other models of concurrency XIII
Transactions on Petri Nets and Other Models of Concurrency XIII
Transactions on Petri Nets and Other Models of Concurrency XIII, pp.27-51, 2018
Transactions on Petri Nets and Other Models of Concurrency XIII ISBN: 9783662583807
Local Process Models (LPMs) describe structured fragments of process behavior that occur in the context of business processes. Traditional support-based LPM discovery aims to generate a collection of process models that describe highly frequent behav
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::af6fe71cd066c389374dc9dfb6b5cf4e
https://research.tue.nl/nl/publications/05f892f1-7731-4427-bb4c-d3a2930411d3
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3
An experimental evaluation of the generalizing capabilities of process discovery techniques and black-box sequence models
Autor: Tax, N., van Zelst, S.J., Teinemaa, I., Reinhartz-Berger, Iris, Guerreiro, Sergio, Guedria, Wided, Schmidt, Rainer, Bera, Palash, Gulden, Jens
Publikováno v: Enterprise, Business-Process and Information Systems Modeling: 19th International Conference, BPMDS 2018, 23rd International Conference, EMMSAD 2018, Held at CAiSE 2018, Tallinn, Estonia, June 11-12, 2018, Proceedings, 165-180
STARTPAGE=165;ENDPAGE=180;TITLE=Enterprise, Business-Process and Information Systems Modeling
Enterprise, Business-Process and Information Systems Modeling ISBN: 9783319917030
BPMDS/EMMSAD@CAiSE
A plethora of automated process discovery techniques have been developed which aim to discover a process model based on event data originating from the execution of business processes. The aim of the discovered process models is to describe the contr
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c25f76ac79a1065a2ca3167e72bd3628
https://research.tue.nl/nl/publications/09b2ee85-c55c-4035-b7ca-314d719b4e1d
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4
Event abstraction for process mining using supervised learning techniques
Autor: Tax, N., Sidorova, N., Haakma, R., van der Aalst, W.M.P., Bi, Y., Kapoor, S., Bhatia, R.
Publikováno v: Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016 ISBN: 9783319569932
Proceedings of the SAI Intelligent Systems Conference (IntelliSys 2016), 21-22 September 2016, London, United Kingdom, 251-269
STARTPAGE=251;ENDPAGE=269;TITLE=Proceedings of the SAI Intelligent Systems Conference (IntelliSys 2016), 21-22 September 2016, London, United Kingdom
Process mining techniques focus on extracting insight in processes from event logs. In many cases, events recorded in the event log are too fine-grained, causing process discovery algorithms to discover incomprehensible process models or process mode
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2aebf87d16df6308a8cce5155eccb5fe
https://research.tue.nl/en/publications/e54eef50-2c42-4ddb-a3c0-1b4571b47640
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5
Mining Process Model Descriptions of Daily Life Through Event Abstraction
Autor: Tax, N., Sidorova, N., Haakma, Reinder, van der Aalst, W.M.P., Kapoor, Supriya, Bhatia, Rahul, Bi, Yaxin
Publikováno v: Studies in Computational Intelligence ISBN: 9783319692654
Intelligent Systems and Applications: Extended and Selected Results from the SAI Intelligent Systems Conference (IntelliSys) 2016, 83-104
STARTPAGE=83;ENDPAGE=104;TITLE=Intelligent Systems and Applications
Process mining techniques focus on extracting insight in processes from event logs. Process mining has the potential to provide valuable insights in (un)healthy habits and to contribute to ambient assisted living solutions when applied on data from s
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6b4acd2b62e528ccd1285fe00e363ac5
https://doi.org/10.1007/978-3-319-69266-1_5
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6
On the use of hierarchical subtrace mining for efficient local process model mining
Autor: Tax, N., Genga, L., Zannone, N., Ceravolo, P., van Keulen, M., Stoffel, K.
Publikováno v: Proceedings of the 7th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2017), 8-22
STARTPAGE=8;ENDPAGE=22;TITLE=Proceedings of the 7th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2017)
Mining local patterns of process behavior is a vital tool for the analysis of event data that originates from flexible processes, for which it is generally not possible to describe the behavior of the process in a single process model without overgen
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=narcis______::f9666b5a2ec8161d7440c099b63cee07
https://research.tue.nl/nl/publications/7d564d78-16be-4a65-b5b7-879e957e20f8
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7
Ranking learning-to-rank methods
Autor: Hiemstra, D., Tax, N., Bockting, S., Ferro, N., Lucchese, C., Maistro, M., Perego, R.
Publikováno v: Proceedings of the 1st International Workshop on LEARning Next gEneration Rankers, 3-3
STARTPAGE=3;ENDPAGE=3;TITLE=Proceedings of the 1st International Workshop on LEARning Next gEneration Rankers
We present a cross-benchmark comparison of learning-to-rank methods using two evaluation measures: the Normalized Winning Number and the Ideal Winning Number. Evaluation results of 87 learning-to-rank methods on 20 datasets show that ListNet, SmoothR
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=narcis______::d53d7f8e0210ded284fb442b7567361c
https://research.tue.nl/nl/publications/568779dc-0133-4503-bca7-261b324d4782
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8
Local process models: pattern mining with process models
Autor: Tax, N., Sidorova, N., van der Aalst, W.M.P., Duivesteijn, W., Pechenizkiy, M., Fletcher, G., Menkovski, V., Postma, E., Vanschoren, J., van der Putten, P.
Publikováno v: Proceedings of the Twenty-Sixth Benelux Conference on Machine Learning (BENELEARN), 83-86
STARTPAGE=83;ENDPAGE=86;TITLE=Proceedings of the Twenty-Sixth Benelux Conference on Machine Learning (BENELEARN)
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=narcis______::f3627349d16ff52a3731d7d286042a67
https://research.tue.nl/nl/publications/4a5901d9-2c16-4f5a-a006-dc0abad23bfc
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9
Predictive business process monitoring with LSTMs
Autor: Tax, N., Verenich, I., La Rosa, M., Dumas, M., Duivesteijn, W., Pechenizkiy, M., Fletcher, G., Menkovski, V., Postma, E., Vanschoren, J., van der Putten, P.
Publikováno v: Proceedings of the Twenty-Sixth Benelux Conference on Machine Learning (BENELEARN), 170-172
STARTPAGE=170;ENDPAGE=172;TITLE=Proceedings of the Twenty-Sixth Benelux Conference on Machine Learning (BENELEARN)
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=narcis______::005b9b09f2e7920d204e0a6d069e9f75
https://research.tue.nl/nl/publications/6abbc126-08cc-4744-9d63-f1b30af5e521
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10
Predictive business process monitoring with LSTM neural networks
Autor: Tax, N., Verenich, I., La Rosa, M., Dumas, M., Pohl, Klaus, Dubois, Eric
Publikováno v: Advanced Information Systems Engineering
Advanced Information Systems Engineering ISBN: 9783319595351
Advanced Information Systems Engineering: 29th International Conference, CAiSE 2017, Proceedings (Lecture Notes in Computer Science, Volume 10253)
Advanced Information Systems Engineering : 29th International Conference, CAiSE 2017, Essen Germany, June 12-16, 2017. Proceedings, 477-492
STARTPAGE=477;ENDPAGE=492;TITLE=Advanced Information Systems Engineering : 29th International Conference, CAiSE 2017, Essen Germany, June 12-16, 2017. Proceedings
Lecture Notes in Computer Science
Lecture Notes in Computer Science-Advanced Information Systems Engineering
Predictive business process monitoring methods exploit logs of completed cases of a process in order to make predictions about running cases thereof. Existing methods in this space are tailor-made for specific prediction tasks. Moreover, their relati
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::907746eb36d0b374cceb115d0d747704
https://doi.org/10.1007/978-3-319-59536-8_30
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