About: Cherry-Picking from Spaghetti: Multi-range Filtering of Event Logs

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Cherry-Picking from Spaghetti: Multi-range Filtering of Event Logs
Creator	Bala, Saimir Djurica, Djordje Mendling, Jan Vidgof, Maxim
Source	PMC
abstract	Mining real-life event logs results into process models which provide little value to the process analyst without support for handling complexity. Filtering techniques are specifically helpful to tackle this problem. These techniques have been focusing on leaving out infrequent aspects of the process which are considered outliers. However, it is exactly in these outliers where it is possible to gather important insights on the process. This paper addresses this problem by defining multi-range filtering. Our technique not only allows to combine both frequent and non-frequent aspects of the process but it supports any user-defined intervals of frequency of activities and variants. We evaluate our approach through a prototype based on the PM4Py library and show the benefits in comparison to existing filtering techniques.
has issue date	2020-05-05(xsd:dateTime)
bibo:doi	10.1007/978-3-030-49418-6_9
has license	no-cc
sha1sum (hex)	4c0449db54626751062e6c3d06cec2ff155d09fb
schema:url	https://doi.org/10.1007/978-3-030-49418-6_9
resource representing a document's title	Cherry-Picking from Spaghetti: Multi-range Filtering of Event Logs
has PubMed Central identifier	PMC7254554
schema:publication	Enterprise, Business-Process and Information Systems Modeling
resource representing a document's body	covid:4c0449db54626751062e6c3d06cec2ff155d09fb#body_text
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