OTM Confederated International Conferences "On the Move to Meaningful Internet Systems"

On the Move to Meaningful Internet Systems: OTM 2015 Conferences pp 320-328 | Cite as

A Probabilistic Unified Framework for Event Abstraction and Process Detection from Log Data

  • Bettina Fazzinga
  • Sergio Flesca
  • Filippo Furfaro
  • Elio Masciari
  • Luigi Pontieri
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9415)

Abstract

We consider the scenario where the executions of different business processes are traced into a log, where each trace describes a process instance as a sequence of low-level events (representing basic kinds of operations). In this context, we address a novel problem: given a description of the processes’ behaviors in terms of high-level activities (instead of low-level events), and in the presence of uncertainty in the mapping between events and activities, find all the interpretations of each trace \(\Phi \). Specifically, an interpretation is a pair \(\langle \sigma , W \rangle \) that provides a two-level “explanation” for \(\Phi \): \(\sigma \) is a sequence of activities that may have triggered the events in \(\Phi \), and W is a process whose model admits \(\sigma \). To solve this problem, we propose a probabilistic framework representing “consistent” \(\Phi \)’s interpretations, where each interpretation is associated with a probability score.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Van der Aalst, W., Weijters, T., Maruster, L.: Workflow mining: Discovering process models from event logs. IEEE TKDE 16(9), 1128–1142 (2004)Google Scholar
  2. 2.
    van der Aalst, W.M.P.: Process Mining: Discovery, Conformance and Enhancement of Business Processes, 1st edn. Springer Publishing Company, Incorporated (2011)Google Scholar
  3. 3.
    van der Aalst, W.M.P., de Beer, H.T., van Dongen, B.F.: Process mining and verification of properties: an approach based on temporal logic. In: Meersman, R., Tari, Z. (eds.) OTM 2005. LNCS, vol. 3760, pp. 130–147. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  4. 4.
    Baier, T., Di Ciccio, C., Mendling, J., Weske, M.: Matching of events and activities - an approach using declarative modeling constraints. In: Gaaloul, K., Schmidt, R., Nurcan, S., Guerreiro, S., Ma, Q. (eds.) BPMDS 2015 and EMMSAD 2015. LNBIP, vol. 214, pp. 119–134. Springer, Heidelberg (2015) CrossRefGoogle Scholar
  5. 5.
    Baier, T., Mendling, J., Weske, M.: Bridging abstraction layers in process mining. Information Systems 46, 123–139 (2014)CrossRefGoogle Scholar
  6. 6.
    Baier, T., Rogge-Solti, A., Weske, M., Mendling, J.: Matching of events and activities - an approach based on constraint satisfaction. In: Frank, U., Loucopoulos, P., Pastor, Ó., Petrounias, I. (eds.) PoEM 2014. LNBIP, vol. 197, pp. 58–72. Springer, Heidelberg (2014) Google Scholar
  7. 7.
    Rozinat, A., van der Aalst, W.M.: Conformance checking of processes based on monitoring real behavior. Information Systems 33(1), 64–95 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Bettina Fazzinga
    • 2
  • Sergio Flesca
    • 1
  • Filippo Furfaro
    • 1
  • Elio Masciari
    • 2
  • Luigi Pontieri
    • 2
  1. 1.DIMESUniversity of CalabriaRendeItaly
  2. 2.ICAR-CNRRendeItaly

Personalised recommendations