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dc.contributor.authorBottrighi, Alessio
dc.contributor.authorCanensi, Luca
dc.contributor.authorLeonardi, Giorgio
dc.contributor.authorMontani, Stefania
dc.contributor.authorTerenziani, Paolo
dc.date.accessioned2021-03-14T23:04:19Z
dc.date.available2021-03-14T23:04:19Z
dc.date.issued2016
dc.identifier.issn0957-4174en_US
dc.identifier.doi10.1016/j.eswa.2015.12.002en_US
dc.identifier.urihttp://hdl.handle.net/10072/403127
dc.description.abstractOperational support assists users while process instances are being executed, by making predictions about the instance completion, or recommending suitable actions, resources or routing decisions, on the basis of the already completed instances, stored as execution traces in the event log. In this paper, we propose a case-based retrieval approach to business process management operational support, where log traces are exploited as cases. Once past traces have been retrieved, classical statistical techniques can be applied to them, to support prediction and recommendation. The framework enables the user to submit queries able to express complex patterns exhibited by the current process instance. Such queries can be composed by several simple patterns (i.e., single actions, or direct sequences of actions), separated by delays (i.e., actions we do not care about). Delays can also be imprecise (i.e., the number of actions can be given as a range). The tool also relies on a tree structure, adopted as an index for a quick retrieval from the available event log. Our approach is highly innovative with respect to the existing literature panorama, since it is the first work that exploits case-based retrieval techniques in the operational support context; moreover, the possibility of retrieving traces by querying complex patterns and the indexing strategy are major departures also with respect to other existing trace retrieval tools proposed in the case based reasoning area. Thanks to its characteristics and methodological solutions, the tool implements operational support tasks in a flexible and efficient way, as demonstrated by our experimental results.en_US
dc.description.peerreviewedYesen_US
dc.languageEnglishen_US
dc.publisherElsevieren_US
dc.relation.ispartofpagefrom212en_US
dc.relation.ispartofpageto221en_US
dc.relation.ispartofjournalExpert Systems with Applicationsen_US
dc.relation.ispartofvolume55en_US
dc.subject.fieldofresearchMathematical Sciencesen_US
dc.subject.fieldofresearchInformation and Computing Sciencesen_US
dc.subject.fieldofresearchEngineeringen_US
dc.subject.fieldofresearchcode01en_US
dc.subject.fieldofresearchcode08en_US
dc.subject.fieldofresearchcode09en_US
dc.subject.keywordsScience & Technologyen_US
dc.subject.keywordsEngineering, Electrical & Electronicen_US
dc.subject.keywordsOperations Research & Management Scienceen_US
dc.subject.keywordsArtificial Intelligenceen_US
dc.titleTrace retrieval for business process operational supporten_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Articlesen_US
dcterms.bibliographicCitationBottrighi, A; Canensi, L; Leonardi, G; Montani, S; Terenziani, P, Trace retrieval for business process operational support, Expert Systems with Applications, 2016, 55, pp. 212-221en_US
dcterms.licensehttps://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.date.updated2021-03-14T23:01:46Z
dc.description.versionAccepted Manuscript (AM)en_US
gro.rights.copyright© 2016 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.en_US
gro.hasfulltextFull Text
gro.griffith.authorTerenziani, Paolo


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