Discovering Context-Aware Models for Predicting Business Process Performances (Contributo in atti di convegno)

Type
Label
  • Discovering Context-Aware Models for Predicting Business Process Performances (Contributo in atti di convegno) (literal)
Anno
  • 2012-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/978-3-642-33606-5_18 (literal)
Alternative label
  • Francesco Folino and Massimo Guarascio and Luigi Pontieri (2012)
    Discovering Context-Aware Models for Predicting Business Process Performances
    in COOPERATIVE INFORMATION SYSTEMS (CoopIS 2012), On the Move to Meaningful Internet Systems -- within OTM 2012 Confederated International Conferences, Rome, Italy, September 10-14, 2012
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Francesco Folino and Massimo Guarascio and Luigi Pontieri (literal)
Pagina inizio
  • 287 (literal)
Pagina fine
  • 304 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://link.springer.com/chapter/10.1007%2F978-3-642-33606-5_18 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • OTM Conferences (1) -- On the Move to Meaningful Internet Systems: OTM 2012, Confederated International Conferences: CoopIS, DOA-SVI, and ODBASE 2012, Rome, Italy, September 10-14, 2012. Proceedings, Part I (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 7565 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana
  • 7565 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 18 (literal)
Note
  • CiteSeerX (literal)
  • SpringerLink (literal)
  • Scopu (literal)
  • DBLP (literal)
  • ACM DL (literal)
  • Google Scholar (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • ICAR-CNR, Rende, Italy. (literal)
Titolo
  • Discovering Context-Aware Models for Predicting Business Process Performances (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-3-642-33605-8 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
  • Robert Meersman and Herv{\'e} Panetto and Tharam S. Dillon and Stefanie Rinderle-Ma and Peter Dadam and Xiaofang Zhou and Siani Pearson and Alois Ferscha and Sonia Bergamaschi and Isabel F. Cruz (literal)
Abstract
  • Discovering predictive models for run-time support is an emerging topic in Process Mining research, which can effectively help optimize business process enactments. However, making accurate estimates is not easy especially when considering fine-grain performance measures (e.g., processing times) on a complex and flexible business process, where performance patterns change over time, depending on both case properties and context factors (e.g., seasonality, workload). We try to face such a situation by using an ad-hoc predictive clustering approach, where different context-related execution scenarios are discovered and modeled accurately via distinct state-aware performance predictors. A readable predictive model is obtained eventually, which can make performance forecasts for any new running process case, by using the predictor of the cluster it is estimated to belong to. The approach was implemented in a system prototype, and validated on a real-life context. Test results confirmed the scalability of the approach, and its efficacy in predicting processing times and associated SLA violations. (literal)
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