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.
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Notes
- 1.
We used an H2 database server with 64 GB of RAM and 8 CPU cores@2.40 Ghz. Discovery was done on a PC with 8 GB of RAM and 2 CPU cores@2.30 Ghz.
- 2.
Due to the fact that the nested query is so time-consuming, we did not include it in some of the tests.
- 3.
Note that the linearithmic comes from the fact that H2 database uses B-tree index, hence finding an element is \(\mathcal {O}(\log {}x)\). There are x rows for which we need to perform this look up, therefore the complexity is \(\mathcal {O}(x \cdot \log {}x)\).
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Syamsiyah, A., van Dongen, B.F., Dijkman, R.M. (2018). A Native Operator for Process Discovery. In: Hartmann, S., Ma, H., Hameurlain, A., Pernul, G., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2018. Lecture Notes in Computer Science(), vol 11030. Springer, Cham. https://doi.org/10.1007/978-3-319-98812-2_25
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