Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/17963
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dc.contributor.authorJOUCK, Toon-
dc.contributor.authorDEPAIRE, Benoit-
dc.date.accessioned2014-12-15T10:40:24Z-
dc.date.available2014-12-15T10:40:24Z-
dc.date.issued2014-
dc.identifier.citationCeravolo, Paolo; Accorsi, Rafael; Russo, Barbara (Ed.). Proceedings of the 4th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2014), p. 174-178-
dc.identifier.issn1613-0073-
dc.identifier.urihttp://hdl.handle.net/1942/17963-
dc.description.abstractPast research revealed issues with artificial event data used for comparative analysis of process mining algorithms. The aim of this research is to design, implement and validate a framework for producing artificial event logs which should increase discriminatory power of artificial event logs when evaluating process discovery techniques.-
dc.language.isoen-
dc.publisherCEUR-
dc.relation.ispartofseriesCEUR Workshop Proceedings-
dc.rightsCreative Commons License-
dc.subject.otherartificial event logs; event log simulation; performance measurement of business processes-
dc.titleGenerating Artificial Event Logs with Sufficient Discriminatory Power to Compare Process Discovery Techniques-
dc.typeProceedings Paper-
local.bibliographicCitation.authorsCeravolo, Paolo-
local.bibliographicCitation.authorsAccorsi, Rafael-
local.bibliographicCitation.authorsRusso, Barbara-
local.bibliographicCitation.conferencedateNovember 19-21, 2014-
local.bibliographicCitation.conferencenameIFIP 2.6 International Symposium on Data-Driven Process Discovery and Analysis-
local.bibliographicCitation.conferenceplaceMilan (Italy)-
dc.identifier.epage178-
dc.identifier.spage174-
local.bibliographicCitation.jcatC1-
dc.description.notesThis paper is a research plan-
local.publisher.placeMilan-
dc.relation.references[1] W. Van der Aalst, T. Weijters, and L. Maruster, “Workflow mining: Discovering process models from event logs,” Knowledge and Data Engineering, IEEE Transactions on, vol. 16, no. 9, pp. 1128–1142, 2004. [2] A. K. A. de Medeiros, A. J. Weijters, and W. M. van der Aalst, “Genetic process mining: an experimental evaluation,” Data Mining and Knowledge Discovery, vol. 14, no. 2, pp. 245–304, 2007. [3] J. De Weerdt, M. De Backer, J. Vanthienen, and B. Baesens, “A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs,” Information Systems, vol. 37, no. 7, pp. 654–676, Nov. 2012. [4] A. Rozinat, A. A. de Medeiros, C. W. Günther, A. Weijters, and W. M. van der Aalst, “Towards an evaluation framework for process mining algorithms,” BPM Center Report, vol. BPM-07–06, 2007. [5] S. K. vanden Broucke, C. Delvaux, J. Freitas, T. Rogova, J. Vanthienen, and B. Baesens, “Uncovering the relationship between event log characteristics and process discovery techniques,” in Business Process Management Workshops, 2014, pp. 41–53. [6] A. Burattin and A. Sperduti, “PLG: a Process Log Generator,” Tech. rep, 2010. [7] T. Jin, J. Wang, and L. Wen, “Efficiently Querying Business Process Models with BeehiveZ.,” in BPM (Demos), 2011.-
local.type.refereedRefereed-
local.type.specifiedProceedings Paper-
local.relation.ispartofseriesnr1293-
local.identifier.vabbc:vabb:378478-
dc.identifier.urlhttp://ceur-ws.org/Vol-1293/-
local.bibliographicCitation.btitleProceedings of the 4th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2014)-
item.accessRightsOpen Access-
item.fullcitationJOUCK, Toon & DEPAIRE, Benoit (2014) Generating Artificial Event Logs with Sufficient Discriminatory Power to Compare Process Discovery Techniques. In: Ceravolo, Paolo; Accorsi, Rafael; Russo, Barbara (Ed.). Proceedings of the 4th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2014), p. 174-178.-
item.fulltextWith Fulltext-
item.validationvabb 2016-
item.contributorJOUCK, Toon-
item.contributorDEPAIRE, Benoit-
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