Abstract
With the abundance of event data, the challenge of enabling process discovery in the large has attracted the community attention. Several works addressed the problem by performing process discovery directly on relational databases, instead of the traditional file based computations. Preliminary results show that moving (parts of) process discovery to the database engine outperforms file based computations. However, all existing works consider the traditional storage of event data which assumes that a clear and predefined process instance notion exists, and that events are correlated to one process instance. In this work, we go two steps further. First, we address the problem of process discovery on object-centric event data which allows several process instance notions to be flexibly defined. We refer to it as multi-process discovery Second, motivated by the intrinsic nature of process discovery that searches for relationships in event data, we address the question of how graph-based storage of object-centric event data improves the performance of multi-process discovery? We propose in-database process discovery operators based on labeled property graphs. We use Neo4j as a DBMS and Cypher as a query language. We compare different discovery strategies that involve graph and relational databases. Our results show that process discovery in graph databases outperform existing approaches.
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Notes
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Additional properties such as the relation name can be added to ObjectRelation.
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We assume that events are totally ordered. In case this assumption is violated, we choose a random order.
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The merging can be performed on other events’ properties. This concept is known as classifier in process mining.
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Available at: https://github.com/Noureldin-Ali/GraphProcessDiscovery.
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For composite case notions, the on-the-fly approach works only with the intersection operator. The union operator requires ordering events and therefore pre-computing the traces.
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In [7], the authors exclude the time to retrieve data from the hard disk.
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Because of space constraints, we do not show all results. All remaining results are available at: https://github.com/Noureldin-Ali/GraphProcessDiscovery.
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Nour Eldin, A., Assy, N., Kobeissi, M., Baudot, J., Gaaloul, W. (2022). Enabling Multi-process Discovery on Graph Databases. In: Sellami, M., Ceravolo, P., Reijers, H.A., Gaaloul, W., Panetto, H. (eds) Cooperative Information Systems. CoopIS 2022. Lecture Notes in Computer Science, vol 13591. Springer, Cham. https://doi.org/10.1007/978-3-031-17834-4_7
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