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A Framework for Human-in-the-loop Monitoring of Concept-drift Detection in Event Log Stream

Authors:

Sylvio Barbon Junior,

Gabriel Marques Tavares,

Victor G. Turrisi da Costa,

Paolo Ceravolo,

Ernesto DamianiAuthors Info & Claims

WWW '18: Companion Proceedings of the The Web Conference 2018

Pages 319 - 326

https://doi.org/10.1145/3184558.3186343

Published: 23 April 2018 Publication History

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Abstract

One of the main challenges of Cognitive Computing (CC) is reacting to evolving environments in near-real time. Therefore, it is expected that CC models provide solutions by examining a summary of past history, rather than using full historical data. This strategy has significant benefits in terms of response time and space complexity but poses new challenges in term of concept-drift detection, where both long term and short terms dynamics should be taken into account. In this paper, we introduce the Concept-Drift in Event Stream Framework (CDESF) that addresses some of these challenges for data streams recording the execution of a Web-based business process. Thanks to CDESF support for feature transformation, we perform density clustering in the transformed feature space of the process event stream, observe track concept-drift over time and identify anomalous cases in the form of outliers. We validate our approach using logs of an e-healthcare process.

References

[1]

Kristof Böhmer and Stefanie Rinderle-Ma. 2016. Automatic signature generation for anomaly detection in business process instance data. In International Workshop on Business Process Modeling, Development and Support. Springer, 196--211.

[2]

RP Jagadeesh Chandra Bose and Wil MP van der Aalst. 2010. Trace Alignment in Process Mining: Opportunities for Process Diagnostics. In BPM, Vol. 6336. Springer, 227--242.

Digital Library

[3]

RP Jagadeesh Chandra Bose, Wil MP van der Aalst, Indre Zliobaite, and Mykola Pechenizkiy. 2011. Handling concept drift in process mining. In International Conference on Advanced Information Systems Engineering. Springer, 391--405.

Digital Library

[4]

Markus Breunig, Hans-Peter Kriegel, Raymond Ng, and Jörg Sander. 1999. Opticsof: Identifying local outliers. Principles of data mining and knowledge discovery (1999), 262--270.

Digital Library

[5]

Andrea Burattin, Marta Cimitile, Fabrizio M Maggi, and Alessandro Sperduti. 2015. Online discovery of declarative process models from event streams. IEEE Transactions on Services Computing 8, 6 (2015), 833--846.

[6]

Andrea Burattin, Alessandro Sperduti, and Wil MP van der Aalst. 2014. Controlflow discovery from event streams. In Evolutionary Computation (CEC), 2014 IEEE Congress on. IEEE, 2420--2427.

[7]

Paolo Ceravolo, Ernesto Damiani, Mohammadsadegh Torabi, and Sylvio Barbon. 2017. Toward a New Generation of Log Pre-processing Methods for Process Mining. In International Conference on Business Process Management. Springer, 55--70.

[8]

Paolo Ceravolo, Ernesto Damiani, and Marco Viviani. 2005. Adding a peerto-peer trust layer to metadata generators. In OTM Confederated International Conferences" On the Move to Meaningful Internet Systems". Springer, 809--815.

Digital Library

[9]

Yixin Chen and Li Tu. 2007. Density-based clustering for real-time stream data. Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining KDD 07 d (2007), 133.

Digital Library

[10]

RA DeCarlo, J Murray, and R Saeks. 1977. Multivariable nyquist theory. Internat. J. Control 25, 5 (1977), 657--675.

[11]

Pedro Domingos and Geoff Hulten. 2000. Mining high-speed data streams. Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining (2000), 71--80.

Digital Library

[12]

Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu. 1996. A Densitybased Algorithm for Discovering Clusters a Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD). AAAI Press, 226--231.

Digital Library

[13]

Michael Frigge, David C Hoaglin, and Boris Iglewicz. 1989. Some implementations of the boxplot. The American Statistician 43, 1 (1989), 50--54.

[14]

João Gama, Pedro Pereira Rodrigues, Eduardo Spinosa, and Andre Carvalho. 2010. Knowledge Discovery from Data Streams. Web Intelligence and Security - Advances in Data and Text Mining Techniques for Detecting and Preventing Terrorist Activities on the Web (2010), 125--138.

[15]

João Gama, Indre Zliobaite, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia. 2014. A survey on concept drift adaptation. ACM Computing Surveys (CSUR) 46, 4 (2014), 44.

Digital Library

[16]

Xiaoxi Huang, Huaxin Huang, Beishui Liao, and Cihua Xu. 2013. An ontologybased approach to metaphor cognitive computation. Minds and Machines 23, 1 (2013), 105--121.

Digital Library

[17]

Imen Khamassi, Moamar Sayed-Mouchaweh, Moez Hammami, and Khaled Ghédira. 2015. Self-adaptive windowing approach for handling complex concept drift. Cognitive Computation 7, 6 (2015), 772--790.

[18]

B Krawczyk, LL Minku, J Gama, and J Stefanowski. 2017. Ensemble learning for data stream analysis: A survey. Information (2017), 1--86.

Digital Library

[19]

Bartosz Krawczyk, Leandro L Minku, João Gama, Jerzy Stefanowski, and Michal Wozniak. 2017. Ensemble learning for data stream analysis: a survey. Information Fusion 37 (2017), 132--156.

Digital Library

[20]

Luc Lévesque. 2014. Nyquist sampling theorem: understanding the illusion of a spinning wheel captured with a video camera. Physics Education 49, 6 (2014), 697--705.

[21]

Felix Mannhardt, Massimiliano de Leoni, Hajo A Reijers, and Wil MP van der Aalst. 2017. Data-Driven Process Discovery-Revealing Conditional Infrequent Behavior from Event Logs. In International Conference on Advanced Information Systems Engineering. Springer, 545--560.

[22]

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12 (2011), 2825--2830.

Digital Library

[23]

Sridhar Ramaswamy, Rajeev Rastogi, and Kyuseok Shim. 2000. Efficient algorithms for mining outliers from large data sets. In ACM Sigmod Record, Vol. 29. ACM, 427--438.

Digital Library

[24]

Maximilian Röglinger, Johannes Seyfried, Simon Stelzl, and Michael zur Muehlen. 2017. Cognitive Computing: What's in for Business Process Management An Exploration of Use Case Ideas. (2017).

[25]

Tegjyot Singh Sethi and Mehmed Kantardzic. 2017. On the reliable detection of concept drift from streaming unlabeled data. Expert Systems with Applications 82 (2017), 77--99.

Digital Library

[26]

Wil MP van der Aalst. 2016. Process mining: data science in action. Springer.

Digital Library

[27]

Wil M. P. van der Aalst. 2011. Process Mining: Discovery, Conformance and Enhancement of Business Processes (1st ed.). Springer Publishing Company, Incorporated.

[28]

Robert A. Wagner and Michael J. Fischer. 1974. The String-to-String Correction Problem. J. ACM 21, 1 (Jan. 1974), 168--173.

Digital Library

[29]

Mingxi Wu and Christopher Jermaine. 2006. Outlier detection by sampling with accuracy guarantees. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 767--772.

Digital Library

[30]

Yingxiu Zhao, Jinhai Li, Wenqi Liu, and Weihua Xu. 2017. Cognitive concept learning from incomplete information. International Journal of Machine Learning and Cybernetics 8, 1 (2017), 159--170.

Cited By

Rong XZhao HCao JChen Q(2025)Time Drift Detection Method Based on Isolation Forest AlgorithmComputer Supported Cooperative Work and Social Computing10.1007/978-981-96-2376-1_8(104-118)Online publication date: 5-Mar-2025
https://doi.org/10.1007/978-981-96-2376-1_8
Truong HNguyen N(2024)TENSAI - Practical and Responsible Observability for Data Quality-aware Large-scale AnalyticsJournal of Data and Information Quality10.1145/370801416:4(1-43)Online publication date: 10-Dec-2024
https://dl.acm.org/doi/10.1145/3708014
Cao YLin LDi YLi XChen W(2024)MDD: Process Drift Detection in Event Logs Integrating Multiple Perspectives2024 IEEE International Conference on Web Services (ICWS)10.1109/ICWS62655.2024.00135(1125-1134)Online publication date: 7-Jul-2024
https://doi.org/10.1109/ICWS62655.2024.00135
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cover image ACM Other conferences

WWW '18: Companion Proceedings of the The Web Conference 2018

April 2018

2023 pages

ISBN:9781450356404

General Chairs:
Pierre-Antoine Champin
Université Claude Bernard Lyon 1, France
,
Fabien Gandon
Inria, Université Côte d'Azur, CNRS, I3S, France
,
Lionel Médini
Université Claude Bernard Lyon 1, CNRS, LIRIS, France
,
Program Chairs:
Mounia Lalmas
Spotify, UK
,
Panagiotis G. Ipeirotis
New York University, USA

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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IW3C2: International World Wide Web Conference Committee

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 23 April 2018

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Information and Communication Technology (ICT) Fund. ABU DHABI

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WWW '18

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IW3C2

WWW '18: The Web Conference 2018

April 23 - 27, 2018

Lyon, France

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Rong XZhao HCao JChen Q(2025)Time Drift Detection Method Based on Isolation Forest AlgorithmComputer Supported Cooperative Work and Social Computing10.1007/978-981-96-2376-1_8(104-118)Online publication date: 5-Mar-2025
https://doi.org/10.1007/978-981-96-2376-1_8
Truong HNguyen N(2024)TENSAI - Practical and Responsible Observability for Data Quality-aware Large-scale AnalyticsJournal of Data and Information Quality10.1145/370801416:4(1-43)Online publication date: 10-Dec-2024
https://dl.acm.org/doi/10.1145/3708014
Cao YLin LDi YLi XChen W(2024)MDD: Process Drift Detection in Event Logs Integrating Multiple Perspectives2024 IEEE International Conference on Web Services (ICWS)10.1109/ICWS62655.2024.00135(1125-1134)Online publication date: 7-Jul-2024
https://doi.org/10.1109/ICWS62655.2024.00135
Meira Neto Ade Sousa RFantinato MPeres S(2024)Revisiting the Transition Matrix-Based Concept Drift Approach: Improving the Detection Task Reliability Through Additional ExperimentationSN Computer Science10.1007/s42979-023-02536-z5:1Online publication date: 8-Jan-2024
https://dl.acm.org/doi/10.1007/s42979-023-02536-z
Cai RZheng CWang JLi DWang CLi B(2024)Reinforcement Learning-Based Streaming Process Discovery Under Concept DriftAdvanced Information Systems Engineering10.1007/978-3-031-61057-8_4(55-70)Online publication date: 3-Jun-2024
https://doi.org/10.1007/978-3-031-61057-8_4
Tavares GBarbon S(2023)Matching business process behavior with encoding techniques via meta-learning: An anomaly detection studyComputer Science and Information Systems10.2298/CSIS220110005T20:3(1207-1233)Online publication date: 2023
https://doi.org/10.2298/CSIS220110005T
Pasquadibisceglie VAppice ACastellano GFiorentino NMalerba D(2023)STARDUST: A Novel Process Mining Approach to Discover Evolving Models From Trace StreamsIEEE Transactions on Services Computing10.1109/TSC.2022.321550216:4(2970-2984)Online publication date: 1-Jul-2023
https://doi.org/10.1109/TSC.2022.3215502
Neubauer TPeeperkorn JPeres SDe Weerdt JFantinato M(2023)Vector Representation for Business Process: Graph Embedding for Domain Knowledge Integration2023 International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA58977.2023.00087(588-594)Online publication date: 15-Dec-2023
https://doi.org/10.1109/ICMLA58977.2023.00087
Guan WCao JGu YQian S(2023)AIMEDInformation Systems10.1016/j.is.2023.102285119:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.is.2023.102285
Adams Jvan Zelst SRose Tvan der Aalst W(2023)Explainable concept drift in process miningInformation Systems10.1016/j.is.2023.102177114:COnline publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1016/j.is.2023.102177
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