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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/26152

Title: A New Process Discovery Algorithm for Exploratory Data Analysis
Authors: Lieben, Jonas
Issue Date: 2018
Citation: Kirikova, Marite; Lupeikiene, Audrone; Teniente, Ernest (Ed.). Proceedings of the Doctoral Consortium Papers Presented at the 30th International Conference on Advanced Information Systems Engineering (CAiSE 2018),p. 19-27
Series/Report: CEUR Workshop Proceedings
Series/Report no.: 2114
Abstract: The domain of process mining created many discovery techniques which can be used to generate a process representation of the data. However, existing techniques come with a flaw for exploratory data analysis (EDA). They tend to produce process models which contain more process behaviour than is observed in the data and do not optimize for understandability. This severely limits their value for EDA, because only patterns which can be observed from the data should be distilled when performing an EDA. We explain why this limitation is important and give a methodology to overcome this. This methodology describes how a discovery algorithm can be developed that is suitable for EDA.
URI: http://hdl.handle.net/1942/26152
Link to publication: http://ceur-ws.org/Vol-2114/
ISSN: 1613-0073
Category: C1
Type: Proceedings Paper
Appears in Collections: Research publications

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