Theory of Computing Systems

, Volume 52, Issue 3, pp 367–402 | Cite as

Querying Probabilistic Business Processes for Sub-Flows

Article
  • 126 Downloads

Abstract

A Business Process (BP for short) consists of a set of activities which, combined in a flow, achieve some business goal. A given BP may have a large, possibly infinite, number of possible execution flows (EX-flows for short), each having some probability to occur at run time. This paper studies query evaluation over such probabilistic BPs. We focus on two important classes of queries, namely boolean queries that compute the probability that a random EX-flow of a BP satisfies a given property, and projection queries focusing on portions of EX-flows that are of interest to the user. For the latter queries the answer consists of the top-k instances of these portions that are most likely to occur at run-time. We study the complexity of query evaluation for both kinds of queries, showing in particular that projection queries may be harder to evaluate than boolean queries. We present a picture of which combinations of BP classes and query features lead to PTIME algorithms and which to NP-hard or infeasible problems.

Keywords

Business processes Projection queries Probabilistic models 

Notes

Acknowledgements

The author would like to thank the anonymous reviewers of this paper for insightful comments.

References

  1. 1.
    Abiteboul, S., Senellart, P.: Querying and updating probabilistic information in XML. In: Proc. of EDBT (2006) Google Scholar
  2. 2.
    Abiteboul, S., Kimelfeld, B., Sagiv, Y., Senellart, P.: On the expressiveness of probabilistic XML models. VLDB J. 18(5), 1041–1064 (2009) CrossRefGoogle Scholar
  3. 3.
    Beeri, C., Eyal, A., Kamenkovich, S., Milo, T.: Querying business processes. In: Proc. of VLDB (2006) Google Scholar
  4. 4.
    Benedikt, M., Godefroid, P., Reps, T.: Model checking of unrestricted hierarchical state machines. In: Proc. Of ICALP (2001) Google Scholar
  5. 5.
    Benedikt, M., Kharlamov, E., Olteanu, D., Senellart, P.: Probabilistic XML via Markov chains. Proc. VLDB Endow. 3(1), 770–781 (2010) Google Scholar
  6. 6.
    Blum, L., Cucker, F., Shub, M., Smale, S.: Complexity and Real Computation. Springer, Berlin (1998) CrossRefGoogle Scholar
  7. 7.
    Borges, R.: On the principle of inclusion and exclusion. Journal Periodica Mathematica Hungarica 3(1–2) (1973) Google Scholar
  8. 8.
    Business Process Execution Language for Web Services. http://www.ibm.com/developerworks/library/ws-bpel/
  9. 9.
    Brazdil, T., Kucera, A., Strazovsky, O.: On the decidability of temporal properties of probabilistic pushdown automata. In: Proc. of STACS (2005) Google Scholar
  10. 10.
    Bultan, T., Su, J., Fu, X.: Analyzing conversations of web services. IEEE Internet Computing 10(1) (2006) Google Scholar
  11. 11.
    Canny, J.: Some algebraic and geometric computations in space. In: Proc. of STOC (1988) Google Scholar
  12. 12.
    Cohen, S., Kimelfeld, B., Sagiv, Y.: Incorporating constraints in probabilistic XML. In: Proc. of PODS (2008) Google Scholar
  13. 13.
    Cohn, D., Hull, R.: Business artifacts: a data-centric approach to modeling business operations and processes. IEEE Data Eng. Bull. 32(3) (2009) Google Scholar
  14. 14.
    Courcelle, B.: The monadic second-order logic of graphs. Inf. Comput. 85(1) (1990) Google Scholar
  15. 15.
    Deutch, D., Milo, T.: Type inference and type checking for queries on execution traces. In: Proc. of VLDB (2008) Google Scholar
  16. 16.
    Deutch, D., Milo, T.: Evaluating top-k projection queries over probabilistic business processes. In: Proc. of ICDE (2009) Google Scholar
  17. 17.
    Deutch, D., Milo, T.: Evaluating top-k projection queries over probabilistic business processes. In: Proc. of ICDT (2009) Google Scholar
  18. 18.
    Deutch, D.: Querying probabilistic business processes for sub-flows. In: ICDT, pp. 54–65 (2011) Google Scholar
  19. 19.
    Deutsch, A., Hull, R., Patrizi, F., Vianu, V.: Automatic verification of data-centric business processes. In: ICDT, pp. 252–267 (2009) CrossRefGoogle Scholar
  20. 20.
    Deutsch, A., Vianu, V.: Wave: automatic verification of data-driven web services. IEEE Data Eng. Bull. 31(3), 35–39 (2008) Google Scholar
  21. 21.
    Diniz, P.: Increasing the accuracy of shape and safety analysis of pointer-based codes. In: LCPC (2003) Google Scholar
  22. 22.
    Etessami, K., Yannakakis, M.: Recursive Markov chains, stochastic grammars, and monotone systems of nonlinear equations. JACM 56(1) (2009) Google Scholar
  23. 23.
    Foster, J.N., Green, T.J., Tannen, V.: Annotated XML: queries and provenance. In: PODS (2008) Google Scholar
  24. 24.
    Fritz, C., Hull, R., Su, J.: Automatic construction of simple artifact-based business processes. In: ICDT (2009) Google Scholar
  25. 25.
    Garey, M.R., Graham, R.L., Johnson, D.S.: Some np-complete geometric problems. In: Proc. of STOC (1976) Google Scholar
  26. 26.
    Hull, R., Su, J.: Tools for composite web services: a short overview. SIGMOD Rec. 34(2) (2005) Google Scholar
  27. 27.
    Kimelfeld, B., Sagiv, Y.: Matching twigs in probabilistic XML. In: VLDB (2007) Google Scholar
  28. 28.
    Kucera, A., Esparza, J., Mayr, R.: Model checking probabilistic pushdown automata. Log. Methods Comput. Sci. 2(1) (2006) Google Scholar
  29. 29.
    Meyn, S.P., Tweedie, R.L.: Markov Chains and Stochastic Stability. Springer, Berlin (1993) MATHCrossRefGoogle Scholar
  30. 30.
    Pirolli, P.L.T., Pitkow, J.E.: Distributions of surfers’ paths through the world wide web: empirical characterizations. World Wide Web 2(1–2) (1999) Google Scholar
  31. 31.
    Re, C., Dalvi, N., Suciu, D.: Efficient top-k query evaluation on probabilistic data. In: Proc. of ICDE (2007) Google Scholar
  32. 32.
    Senellart, P., Abiteboul, S.: On the complexity of managing probabilistic XML data. In: PODS (2007) Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Ben Gurion UniversityBeer-ShevaIsrael

Personalised recommendations