Understanding knowlegde-intensive processes: From traces to instance graphs

Claudia Diamantini, Laura Genga, Domenico Potena

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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Abstract

Enterprise information systems, while support daily activities, typically collect data on executed processes in event logs. These data describe the temporal sequence in which activities were carried out, hiding possible parallelism and other control flows. Representing the structure of each process execution in the form of an Instance Graph, enables managers to discover valuable knowledge on enterprise behaviors. In this work, we describe BIG4ProM, a tool which implements the Building Instance Graph (BIG) algorithm. BIG4ProM exploits filtering Process Discovery algorithms implemented in ProM in order to return the set of instance graphs related to the given event log. The plug-in is conceived to support both expert and standard users.

Original languageEnglish
Title of host publication2016 International Conference on High Performance Computing and Simulation, HPCS 2016
EditorsVesna Zeljkovic, Waleed W. Smari
PublisherInstitute of Electrical and Electronics Engineers
Pages218-221
Number of pages4
ISBN (Electronic)9781509020881
DOIs
Publication statusPublished - 13 Sep 2016
Externally publishedYes
Event14th International Conference on High Performance Computing and Simulation, HPCS 2016 - Innsbruck, Austria
Duration: 18 Jul 201622 Jul 2016

Conference

Conference14th International Conference on High Performance Computing and Simulation, HPCS 2016
CountryAustria
CityInnsbruck
Period18/07/1622/07/16

Keywords

  • building instance graph
  • knowledge-intensive process
  • process mining
  • ProM

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