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WIREs Data Mining Knowl Discov
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Extraction, correlation, and abstraction of event data for process mining

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Abstract Process mining provides a rich set of techniques to discover valuable knowledge of business processes based on data that was recorded in different types of information systems. It enables analysis of end‐to‐end processes to facilitate process re‐engineering and process improvement. Process mining techniques rely on the availability of data in the form of event logs. In order to enable process mining in diverse environments, the recorded data need to be located and transformed to event logs. The journey from raw data to event logs suitable for process mining can be addressed by a variety of methods and techniques, which are the focus of this article. In particular, techniques proposed in the literature to support the creation of event logs from raw data are reviewed and classified. This includes techniques for identification and extraction of the required event data from diverse sources as well as their correlation and abstraction. This article is categorized under: Technologies > Structure Discovery and Clustering Fundamental Concepts of Data and Knowledge > Data Concepts Technologies > Data Preprocessing
A simple order handling process model and a collection of event data extracted for this process. The dashed rectangles indicate which elements are correlated as they refer to the same case. The dotted ovals illustrate the abstraction of sets of event data to events that denote activity executions, while the respective activities are highlighted by the the dotted arrows
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Overview of the main concepts related to data and events in process mining. Adapted from Carmona et al. ()
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The spectrum of event abstraction techniques
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The spectrum of event correlation techniques
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The spectrum of incorporated information for event extraction techniques
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Main elements of the eXtensible Event Stream metamodel (Günther & Verbeek, )
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Technologies > Data Preprocessing
Fundamental Concepts of Data and Knowledge > Data Concepts
Technologies > Structure Discovery and Clustering

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