Enhancing Discovered Process Models with Data Object Lifecycles
Journal
Enhancing Discovered Process Models with Data Object Lifecycles
ISSN
2325-6354
ISSN-Digital
2325-6362
Type
conference paper
Date Issued
2021-10-25
Author(s)
Abstract (De)
Process discovery is an important area in the field of process mining, where most discovery algorithms focus on process control-flow, giving little attention to the data-flow perspective. As a result, the discovered process models lack information about data dependencies, and process experts need to manually enrich the discovered process models accordingly. This requires deep domain knowledge, is not scalable, and error-prone. To overcome this limitation, this paper proposes an approach that aims to discover the data objects and their behavior by investigating how event attributes are manipulated during process execution. The resulting data objects are used to enhance the discovered process model. The feasibility of the proposed approach is evaluated with two real-life event logs: Road Traffic Fine Management and Hospital Billing.
Language
English
HSG Classification
contribution to scientific community
Book title
International Conference on Enterprise Distributed Object Computing (EDOC)
Publisher
IEEE
Start page
124
End page
133
Pages
9
Event Title
2021 IEEE 25th International Enterprise Distributed Object Computing Conference
Event Location
Gold Coast, Australia
Event Date
25-29 Oct. 2021
Subject(s)
Eprints ID
265146
File(s)![Thumbnail Image]()
open.access
Name
EDOC1.pdf
Size
1.32 MB
Format
Adobe PDF
Checksum (MD5)
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