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http://hdl.handle.net/1942/26152
Title: | A New Process Discovery Algorithm for Exploratory Data Analysis | Authors: | LIEBEN, Jonas | Issue Date: | 2018 | Source: | 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. | Keywords: | process; exploratory data analysis; comprehensibility; precision; process discovery algorithm | Document URI: | http://hdl.handle.net/1942/26152 | Link to publication/dataset: | http://ceur-ws.org/Vol-2114/ | Rights: | Copyright © 2018 for the individual papers by the papers’ authors. Copying permitted only for private and academic purposes. This volume is published and copyrighted by its editors. | Category: | C1 | Type: | Proceedings Paper | Validations: | vabb 2020 |
Appears in Collections: | Research publications |
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paper3.pdf | Published version | 397.5 kB | Adobe PDF | View/Open |
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