Overlapping analytic stages in online process mining

Gabriel Marques Tavares, Paolo Ceravolo, Victor G.Turrisi Da Costa, Ernesto Damiani, Sylvio Barbon Junior

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

21 Scopus citations

Abstract

Process mining uses business event logs to understand the flow of activities, to identify anomalous cases and to enhance processes. Today, real-time process mining tools mainly deal with a single task at a time (process discovery, conformance checking, process enhancement or concept change detection). In this paper, we introduce an underlined layer overlapping with multiple online process mining tasks to smooth their integration. Following a case clustering approach, based on trace and time analysis, our proposal supports simultaneously?: process discovery, conformance checking, and concept drift detection. We evaluated our approach and compared it with other techniques using both real-life and synthetic data, obtaining promising results.

Original languageBritish English
Title of host publicationProceedings - 2019 IEEE International Conference on Services Computing, SCC 2019 - Part of the 2019 IEEE World Congress on Services
EditorsElisa Bertino, Carl K. Chang, Peter Chen, Ernesto Damiani, Ernesto Damiani, Michael Goul, Katsunori Oyama
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages167-175
Number of pages9
ISBN (Electronic)9781728127200
DOIs
StatePublished - Jul 2019
Event2019 IEEE International Conference on Services Computing, SCC 2019 - Milan, Italy
Duration: 8 Jul 201913 Jul 2019

Publication series

NameProceedings - 2019 IEEE International Conference on Services Computing, SCC 2019 - Part of the 2019 IEEE World Congress on Services

Conference

Conference2019 IEEE International Conference on Services Computing, SCC 2019
Country/TerritoryItaly
CityMilan
Period8/07/1913/07/19

Keywords

  • Anomaly detection
  • Clustering
  • Concept Drift
  • Process Mining
  • Stream Mining

Fingerprint

Dive into the research topics of 'Overlapping analytic stages in online process mining'. Together they form a unique fingerprint.

Cite this