Abstract
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 language | English |
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Title of host publication | Database and Expert Systems Applications - 29th International Conference, DEXA 2018, Proceedings |
Editors | Günther Pernul, Sven Hartmann, Hui Ma, Abdelkader Hameurlain, Roland R. Wagner |
Publisher | Springer |
Pages | 292-300 |
Number of pages | 9 |
ISBN (Print) | 9783319988115 |
DOIs | |
Publication status | Published - 1 Jan 2018 |
Event | 29th International Conference on Database and Expert Systems Applications, (DEXA2018) - Regensburg, Germany Duration: 3 Sep 2018 → 6 Sep 2018 http://www.dexa.org/ |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 11030 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 29th International Conference on Database and Expert Systems Applications, (DEXA2018) |
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Abbreviated title | DEXA2018 |
Country/Territory | Germany |
City | Regensburg |
Period | 3/09/18 → 6/09/18 |
Internet address |
Keywords
- Process discovery
- Relational database
- SQL operator