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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.

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Cited By

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
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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.

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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

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
Bouchal ARadgui M(2023)Online Process Mining: A Systematic Literature ReviewArtificial Intelligence and Green Computing10.1007/978-3-031-46584-0_21(277-288)Online publication date: 4-Nov-2023
https://doi.org/10.1007/978-3-031-46584-0_21
Yang LMcClean SDonnelly MBurke KKhan K(2022)Detecting and Responding to Concept Drift in Business ProcessesAlgorithms10.3390/a1505017415:5(174)Online publication date: 21-May-2022
https://doi.org/10.3390/a15050174
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