A native operator for process discovery

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3 Citations (Scopus)
2 Downloads (Pure)


The goal of process mining is to gain insights into operational processes through the analysis of events recorded by information systems. Typically, this is done in three phases. Firstly, events are extracted from a data store into an event log. Secondly, an intermediate structure is built in memory and finally, this intermediate structure is converted into a process model or other analysis results. In this paper, we propose a native SQL operator for direct process discovery on relational databases. We merge steps 1 and 2 by defining a native operator for the simplest form of the intermediate structure, called the “directly follows relation”. We evaluate our work on big event data and the experimental results show that it performs faster than the state-of-the-art of database approaches.

Original languageEnglish
Title of host publicationDatabase and Expert Systems Applications - 29th International Conference, DEXA 2018, Proceedings
EditorsGünther Pernul, Sven Hartmann, Hui Ma, Abdelkader Hameurlain, Roland R. Wagner
Number of pages9
ISBN (Print)9783319988115
Publication statusPublished - 1 Jan 2018
Event29th International Conference on Database and Expert Systems Applications, (DEXA2018) - Regensburg, Germany
Duration: 3 Sept 20186 Sept 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11030 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference29th International Conference on Database and Expert Systems Applications, (DEXA2018)
Abbreviated titleDEXA2018
Internet address


  • Process discovery
  • Relational database
  • SQL operator


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