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While the sequential, single-dimensional nature of event logs aids querying for (sub)sequences of events based on <jats:italic>temporal relations<\/jats:italic> such as \u201cdirectly\/eventually-follows,\u201d it does not support querying <jats:italic>multi-dimensional<\/jats:italic> event data of multiple related entities. Relational databases allow storing multi-dimensional event data, but existing query languages do not support querying for sequences or paths of events in terms of temporal relations. In this paper, we propose a general data model for multi-dimensional event data based on <jats:italic>labeled property graphs<\/jats:italic> that allows storing structural and temporal relations in a single, integrated graph-based data structure in a systematic way. We provide semantics for all concepts of our data model, and generic queries for modeling event data over multiple entities that <jats:italic>interact synchronously and asynchronously<\/jats:italic>. The queries allow for efficiently converting large real-life event data sets into our data model, and we provide 5 converted data sets for further research. We show that typical and advanced queries for retrieving and aggregating such multi-dimensional event data can be formulated and executed efficiently in the existing query language Cypher, giving rise to several new research questions. Specifically, aggregation queries on our data model enable process mining over multiple inter-related entities using off-the-shelf technology.<\/jats:p>","DOI":"10.1007\/s13740-021-00122-1","type":"journal-article","created":{"date-parts":[[2021,5,27]],"date-time":"2021-05-27T19:02:51Z","timestamp":1622142171000},"page":"109-141","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":78,"title":["Multi-Dimensional Event Data in Graph Databases"],"prefix":"10.1007","volume":"10","author":[{"given":"Stefan","family":"Esser","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1993-9363","authenticated-orcid":false,"given":"Dirk","family":"Fahland","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,27]]},"reference":[{"key":"122_CR1","unstructured":"van der Aalst WMP (2016) Process mining - Data Science in Action, 2nd edn. Springer, pp 3-452. 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