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Generalized alignment-based trace clustering of process behavior
dc.contributor.author | Boltenhagen, Mathilde |
dc.contributor.author | Chatain, Thomas |
dc.contributor.author | Carmona Vargas, Josep |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.date.accessioned | 2020-02-24T10:10:52Z |
dc.date.available | 2020-02-24T10:10:52Z |
dc.date.issued | 2019 |
dc.identifier.citation | Boltenhagen, M.; Chatain, T.; Carmona, J. Generalized alignment-based trace clustering of process behavior. A: International Conference on Applications and Theory of Petri Nets and Concurrency. "Application and Theory of Petri Nets and Concurrency, 40th International Conference, PETRI NETS 2019: Aachen, Germany, June 23–28, 2019: proceedings". Berlín: Springer, 2019, p. 237-257. |
dc.identifier.isbn | 978-3-030-21571-2 |
dc.identifier.uri | http://hdl.handle.net/2117/178380 |
dc.description.abstract | Process mining techniques use event logs containing real process executions in order to mine, align and extend process models. The partition of an event log into trace variants facilitates the understanding and analysis of traces, so it is a common pre-processing in process mining environments. Trace clustering automates this partition; traditionally it has been applied without taking into consideration the availability of a process model. In this paper we extend our previous work on process model based trace clustering, by allowing cluster centroids to have a complex structure, that can range from a partial order, down to a subnet of the initial process model. This way, the new clustering framework presented in this paper is able to cluster together traces that are distant only due to concurrency or loop constructs in process models. We show the complexity analysis of the different instantiations of the trace clustering framework, and have implemented it in a prototype tool that has been tested on different datasets. |
dc.format.extent | 21 p. |
dc.language.iso | eng |
dc.publisher | Springer |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Informàtica teòrica |
dc.subject.lcsh | Data mining |
dc.subject.lcsh | Formal methods (Computer science) |
dc.subject.other | Business data processing |
dc.subject.other | Pattern clustering |
dc.subject.other | System monitoring |
dc.title | Generalized alignment-based trace clustering of process behavior |
dc.type | Conference report |
dc.subject.lemac | Mineria de dades |
dc.subject.lemac | Mètodes formals (Informàtica) |
dc.contributor.group | Universitat Politècnica de Catalunya. ALBCOM - Algorismia, Bioinformàtica, Complexitat i Mètodes Formals |
dc.identifier.doi | 10.1007/978-3-030-21571-2_14 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://link.springer.com/chapter/10.1007/978-3-030-21571-2_14 |
dc.rights.access | Open Access |
local.identifier.drac | 27008864 |
dc.description.version | Postprint (author's final draft) |
dc.relation.projectid | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-86727-C2-1-R/ES/MODELOS Y METODOS BASADOS EN GRAFOS PARA LA COMPUTACION EN GRAN ESCALA/ |
local.citation.author | Boltenhagen, M.; Chatain, T.; Carmona, J. |
local.citation.contributor | International Conference on Applications and Theory of Petri Nets and Concurrency |
local.citation.pubplace | Berlín |
local.citation.publicationName | Application and Theory of Petri Nets and Concurrency, 40th International Conference, PETRI NETS 2019: Aachen, Germany, June 23–28, 2019: proceedings |
local.citation.startingPage | 237 |
local.citation.endingPage | 257 |