Advertisement

Speech Acts Featuring Decisions in Knowledge-Intensive Processes

  • Tatiana Barboza
  • Pedro Richetti
  • Fernanda Baião
  • Flavia Maria Santoro
  • João Carlos Gonçalves
  • Kate Revoredo
  • Anton Yeshchenko
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11230)

Abstract

A Knowledge-Intensive Process (KiP) is specified as a composition of a set of prospective activities (events) whose execution contributes to achieving a goal and whose control-flow, at the instance level, typically presents a high degree of variability among its several past executions. Variability is a consequence of a combination of decision points and informal interactions among participants on collaborative and innovative activities. These interactions may occur through message exchange, thus understanding the interplay of illocutionary acts within messages may bring insights on how participants make decisions. In this paper, we propose mechanisms that identify speech acts in the set of messages that mostly lead to decision points in a KiP providing an understanding of conversational patterns. We empirically evaluate our proposal considering data from a company that provides IT services to several customers.

Keywords

Knowledge-intensive process Decision-making Speech acts 

Notes

Acknowledgments

This work is partially funded by the EU H2020 program under MSCA-RISE agreement 645751 (RISE BPM), FFG Austrian Research Promotion Agency (project number: 866270), and UNIRIO (PQ-UNIRIO N01/2018).

References

  1. 1.
    Di Ciccio, C., Marrella, A., Russo, A.: Knowledge-intensive processes: characteristics, requirements and analysis of contemporary approaches. J. Data Semant. 4(1), 29–57 (2015)CrossRefGoogle Scholar
  2. 2.
    Richter-von Hagen, C., Ratz, D., Povalej, R.: Towards self-organizing knowledge intensive processes. J. Univers Knowl. Manag. 2, 148–169 (2005)Google Scholar
  3. 3.
    dos Santos França, J.B., Netto, J.M., do E. S. Carvalho, J., Santoro, F.M., Baião, F.A., Pimentel, M.: The knowledge-intensive process ontology. Softw. Syst. Model. 14, 1127–1157 (2014)CrossRefGoogle Scholar
  4. 4.
    Guizzardi, G., Guarino, N., Almeida, J.P.A.: Ontological considerations about the representation of events and endurants in business models. In: La Rosa, M., Loos, P., Pastor, O. (eds.) BPM 2016. LNCS, vol. 9850, pp. 20–36. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-45348-4_2CrossRefGoogle Scholar
  5. 5.
    Rozinat, A., van der Aalst, W.M.P.: Decision mining in ProM. In: Dustdar, S., Fiadeiro, J.L., Sheth, A.P. (eds.) BPM 2006. LNCS, vol. 4102, pp. 420–425. Springer, Heidelberg (2006).  https://doi.org/10.1007/11841760_33CrossRefGoogle Scholar
  6. 6.
    Bazhenova, E., Buelow, S., Weske, M.: Discovering decision models from event logs. In: Abramowicz, W., Alt, R., Franczyk, B. (eds.) BIS 2016. LNBIP, vol. 255, pp. 237–251. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-39426-8_19CrossRefGoogle Scholar
  7. 7.
    De Leoni, M., van der Aalst, W.M.P.: Data-aware process mining: discovering decisions in processes using alignments. In: Proceedings of the 28th Annual ACM Symposium on Applied Computing, pp. 1454–1461. ACM (2013)Google Scholar
  8. 8.
    Campos, J., Richetti, P., Baião, F.A., Santoro, F.M.: Discovering business rules in knowledge-intensive processes through decision mining: an experimental study. In: Teniente, E., Weidlich, M. (eds.) BPM 2017. LNBIP, vol. 308, pp. 556–567. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-74030-0_44CrossRefGoogle Scholar
  9. 9.
    De Smedt, J., Hasić, F., vanden Broucke, S.K.L.M., Vanthienen, J.: Towards a holistic discovery of decisions in process-aware information systems. In: Carmona, J., Engels, G., Kumar, A. (eds.) BPM 2017. LNCS, vol. 10445, pp. 183–199. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-65000-5_11CrossRefGoogle Scholar
  10. 10.
    Richetti, P.H.P., de A. R. Gonçalves, J.C., Baião, F.A., Santoro, F.M.: Analysis of knowledge-intensive processes focused on the communication perspective. In: Carmona, J., Engels, G., Kumar, A. (eds.) BPM 2017. LNCS, vol. 10445, pp. 269–285. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-65000-5_16CrossRefGoogle Scholar
  11. 11.
    Bratman, M.: Intention, plans, and practical reason. Harvard University Press, Cambridge (1987)Google Scholar
  12. 12.
    Adam, C., Gaudou, B.: BDI agents in social simulations: a survey. Knowl. Eng. Rev. 31(3), 207–238 (2016)CrossRefGoogle Scholar
  13. 13.
    Balke, T., Gilbert, N.: How do agents make decisions? A survey. J. Artif. Soc. Soc. Simul. 17(4), 13 (2014)CrossRefGoogle Scholar
  14. 14.
    Bach, K., Harnish, R.: Linguistic communication and speech acts (1979)Google Scholar
  15. 15.
    Searle, J.R., Vanderveken, D.: Foundations of Illocutionary Logic. Cambridge University Press, Cambridge (1985)zbMATHGoogle Scholar
  16. 16.
    Austin, J.L.: How To Do Things With Words. Oxford University Press, Oxford (1975)CrossRefGoogle Scholar
  17. 17.
    Reese, M.R.: Natural Language Processing with Java. Packt Publishing. Birmingham, Mumbai (2015). Open Source Community Experience DistilledGoogle Scholar
  18. 18.
    van der Aalst, W.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2016).  https://doi.org/10.1007/978-3-642-19345-3CrossRefzbMATHGoogle Scholar
  19. 19.
    Lu, X., et al.: Semi-supervised log pattern detection and exploration using event concurrence and contextual information. In: Panetto, H., et al. (eds.) OTM 2017. LNCS, vol. 10573, pp. 154–174. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-69462-7_11CrossRefGoogle Scholar
  20. 20.
    Gunnarsson, M.: Group decision making–Language and interaction. Ph.D. thesis, Department of Linguistics, Göteborg University (2006)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Tatiana Barboza
    • 1
  • Pedro Richetti
    • 1
  • Fernanda Baião
    • 1
  • Flavia Maria Santoro
    • 1
  • João Carlos Gonçalves
    • 1
  • Kate Revoredo
    • 1
  • Anton Yeshchenko
    • 2
  1. 1.Federal University of the State of Rio de JaneiroRio de JaneiroBrazil
  2. 2.Vienna University of Economics and Business (WU)ViennaAustria

Personalised recommendations