Published April 21, 2021
| Version 1.0
Dataset
Open
Event Data and Queries for Multi-Dimensional Event Data in the Neo4j Graph Database
Description
Data model and generic query templates for translating and integrating a set of related CSV event logs into a single event graph for as used in https://dx.doi.org/10.1007/s13740-021-00122-1
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 and https://dx.doi.org/10.1007/s13740-021-00122-1
- Fork the query code from Github: https://github.com/multi-dimensional-process-mining/graphdb-eventlogs
Files
csv_to_eventgraph_neo4j_1.0.zip
Files
(229.9 MB)
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md5:45e5b447ff997f374506f25775962934
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Additional details
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