@inbook{6a9ff9f8d9e747e38cb27bd43038a6ff,
title = "Process Mining over Multiple Behavioral Dimensions with Event Knowledge Graphs",
abstract = "Classical process mining relies on the notion of a unique case identifier, which is used to partition event data into independent sequences of events. In this chapter, we study the shortcomings of this approach for event data over multiple entities. We introduce event knowledge graphs as data structure that allows to naturally model behavior over multiple entities as a network of events. We explore how to construct, query, and aggregate event knowledge graphs to get insights into complex behaviors. We will ultimately show that event knowledge graphs are a very versatile tool that opens the door to process mining analyses in multiple behavioral dimensions at once.",
keywords = "Event knowledge graph, Process mining",
author = "Dirk Fahland",
year = "2022",
month = jun,
day = "27",
doi = "10.1007/978-3-031-08848-3_9",
language = "English",
isbn = "978-3-031-08847-6",
series = "Lecture Notes in Business Information Processing",
publisher = "Springer",
pages = "274--319",
editor = "{van der Aalst}, {Wil M.P.} and Josep Carmona",
booktitle = "Process Mining Handbook",
address = "Germany",
}