Discovering Structural Errors From Business Process Event Logs

Wei Song, Zhen Chang, Hans Arno Jacobsen, Pengcheng Zhang

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Process mining aims at discovering behavioral knowledge of business processes from their event logs, which has received an increasing attention in the era of cloud computing and big data. Surprisingly, to date, discovering structural errors (e.g., deadlocks and lack of synchronization) from event logs has not been considered in state-of-the-art process mining techniques. Moreover, existing process discovery approaches cannot be directly applied to event logs of processes with structural errors due to erroneous event occurrences caused by unsynchronized activities. To address this problem, we first preprocess the event log to obtain two separate event logs that are used to discover deadlocks and lack of synchronization, respectively. Erroneous event occurrences caused by unsynchronized activities are discarded in the two processed event logs, from which our error mining algorithms can discover all process fragments involving structural errors, without the need to obtain the overall process first. We implement our approach in a ProM plugin and evaluate it on event logs of real-life business processes, the results of which demonstrate that our approach can effectively and efficiently discover deadlocks and lack of synchronization if event logs contain sufficient event sequences.

Original languageEnglish
Pages (from-to)5293-5306
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume34
Issue number11
DOIs
StatePublished - 1 Nov 2022
Externally publishedYes

Keywords

  • concurrency
  • deadlock
  • event log
  • lack of synchronization
  • log preprocessing
  • Process mining

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