Published May 29, 2020
| Version 0.9
Dataset
Open
Event Data and Queries for Multi-Dimensional Event Data in the Neo4j Graph Database
Description
Datasets and scripts for modeling business process event log data in Neo4j
Provides input data for 5 datasets (BPIC14, BPIC15, BPIC16, BPIC17, BPIC19)
Provides Python scripts to prepare and import each dataset into a Neo4j database instance through Cypher queries, representing behavioral information not globally (as in an event log), but locally per entity and per relation between entities.
Provides Python scripts to retrieve event data from a Neo4j database instance and render it using Graphviz dot.
- The data model and queries are described in detail in: Stefan Esser, Dirk Fahland: Multi-Dimensional Event Data in Graph Databases (2020) https://arxiv.org/abs/2005.14552
- Fork the query code from Github: https://github.com/multi-dimensional-process-mining/graphdb-eventlogs
Files
Logs_for_Neo4J.zip
Files
(229.5 MB)
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md5:35ea0b4b93e2abaf566a282a6a21f050
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Additional details
Related works
- Is previous version of
- Software: 10.5281/zenodo.4708117 (DOI)
References
- Stefan Esser, Dirk Fahland: Multi-Dimensional Event Data in Graph Databases (2020). arXiv: 2005.14552, https://arxiv.org/abs/2005.14552
- Esser, Stefan. (2020, February 19). A Schema Framework for Graph Event Data. Zenodo. https://doi.org/10.5281/zenodo.3820037
- Esser, S., Fahland, D.: Storing and querying multi-dimensional process event logs usinggraph databases. In: C.D. Francescomarino, R.M. Dijkman, U. Zdun (eds.) BusinessProcess Management Workshops - BPM 2019 International Workshops, Vienna, Austria,September 1-6, 2019, Revised Selected Papers,Lecture Notes in Business InformationProcessing, vol. 362, pp. 632–644. Springer (2019). https://doi.org/10.1007/978-3-030-37453-2_51