A conceptual framework for supporting deep exploration of business process behavior

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


Process mining serves for gaining insights into business process behavior based on event logs. These techniques are typically limited to addressing data included in the log. Recent studies suggest extracting data-rich event logs from databases or transaction logs. However, these event logs are at a very fine granularity level, substituting business-level activities by low-level database operations, and challenging data-aware process mining. To address this gap, we propose an approach that enables a broad and deep exploration of process behavior, using a conceptual framework based on three sources: the event log that holds information regarding the business-level activities, the (relational) database that stores the current values of data elements, and the transaction (redo) log that captures historical data operations performed on the database as a result of business process activities. Nine types of operations define how to map subsets of elements among the three sources in order to support human analysts in exploring and understanding the reasons of observed process behavior. A preliminary evaluation analyzes the outcomes for four useful scenarios.

Original languageEnglish
Title of host publicationConceptual Modeling - 37th International Conference, ER 2018, Proceedings
EditorsZhanhuai Li, Juan C. Trujillo, Xiaoyong Du, Mong Li Lee, Karen C. Davis, Tok Wang Ling, Guoliang Li
PublisherSpringer Verlag
Number of pages14
ISBN (Print)9783030008468
StatePublished - 2018
Event37th International Conference on Conceptual Modeling, ER 2018 - Xi'an, China
Duration: 22 Oct 201825 Oct 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11157 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference37th International Conference on Conceptual Modeling, ER 2018

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2018.


  • Business process
  • Data-aware
  • Database
  • Event log
  • Process mining
  • Transaction log

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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