Discovering Data Models from Event Logs

Bano, Dorina; Weske, Mathias

doi:10.1007/978-3-030-62522-1_5

Cited by 8 publications

(1 citation statement)

References 114 publications

(183 reference statements)

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“…They, rather, contain information about certain attributes. It is the focus of this paper to categorize event log attributes into data objects, not just based on static information (see [29]), but on the behavior aspects of these attributes.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Discovered Process Models with Data Object Lifecycles

Bano

Zerbato

Weber

et al. 2021

2021 IEEE 25th International Enterprise Distributed Object Computing Conference (EDOC)

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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.

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Section: Related Workmentioning

confidence: 99%