Enabling efficient process mining on large data sets: realizing an in-database process mining operator

Remco Dijkman (Corresponding author), Juntao Gao, Alifah Syamsiyah, Boudewijn van Dongen, Paul Grefen, Arthur ter Hofstede

Research output: Contribution to journalArticleAcademicpeer-review

12 Citations (Scopus)
58 Downloads (Pure)


Process mining can be used to analyze business processes based on logs of their execution. These execution logs are often obtained by querying a database and storing the results in a file. The mining itself is then done on the file, such that the data processing power of the database cannot be used after the log is extracted. Enabling process mining directly on a database therefore provides additional flexibility and efficiency. To help facilitate this, this paper formally defines a database operator that extracts the ‘directly follows’ relation—one of the relations that is at the heart of process mining—from an operational database. It defines the operator using the well-known relational algebra and formally proves equivalence properties of the operator that are useful for query optimization. Subsequently, it presents time-complexity properties of the operator. Finally, it presents an implementation of the operator as part of the H2 DBMS and demonstrates that this implementation extracts the ‘directly follows’ relation from a database with an arbitrary database structure within a fraction of a second; several orders of magnitude faster than is currently possible.

Original languageEnglish
Pages (from-to)227-253
Number of pages27
JournalDistributed and Parallel Databases
Issue number1
Early online date19 May 2019
Publication statusPublished - 1 Mar 2020


  • Database management system
  • Formal methods
  • Process mining
  • Relational algebra


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