Advertisement

Cardinality Constraints with Probabilistic Intervals

  • Tania Katell Roblot
  • Sebastian Link
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10650)

Abstract

Probabilistic databases accommodate well the requirements of modern applications that produce large volumes of uncertain data from a variety of sources. We propose an expressive class of probabilistic cardinality constraints which empowers users to specify lower and upper bounds on the marginal probabilities by which cardinality constraints should hold in a data set of acceptable quality. The bounds help organizations balance the consistency and completeness targets for their data quality, and provide probabilities on the number of query answers without querying the data. Algorithms are established for an agile schema-driven acquisition of the right lower and upper bounds in a given application domain, and for reasoning about the constraints.

Keywords

Cardinality constraint Data and knowledge intelligence Decision support Probability Requirements engineering Summaries 

References

  1. 1.
    Brown, P., Ganesan, J., Köhler, H., Link, S.: Keys with probabilistic intervals. In: Comyn-Wattiau, I., Tanaka, K., Song, I.-Y., Yamamoto, S., Saeki, M. (eds.) ER 2016. LNCS, vol. 9974, pp. 164–179. Springer, Cham (2016). doi: 10.1007/978-3-319-46397-1_13CrossRefGoogle Scholar
  2. 2.
    Brown, P., Link, S.: Probabilistic keys. IEEE Trans. Knowl. Data Eng. 29(3), 670–682 (2017)CrossRefGoogle Scholar
  3. 3.
    Chen, P.P.: The entity-relationship model - toward a unified view of data. ACM Trans. Database Syst. 1(1), 9–36 (1976)CrossRefGoogle Scholar
  4. 4.
    Fagin, R.: Horn clauses and database dependencies. J. ACM 29(4), 952–985 (1982)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Ferrarotti, F., Hartmann, S., Link, S.: Efficiency frontiers of XML cardinality constraints. Data Knowl. Eng. 87, 297–319 (2013)CrossRefGoogle Scholar
  6. 6.
    Hall, N., Köhler, H., Link, S., Prade, H., Zhou, X.: Cardinality constraints on qualitatively uncertain data. Data Knowl. Eng. 99, 126–150 (2015)CrossRefGoogle Scholar
  7. 7.
    Hartmann, S., Köhler, H., Leck, U., Link, S., Thalheim, B., Wang, J.: Constructing Armstrong tables for general cardinality constraints and not-null constraints. Ann. Math. Artif. Intell. 73(1–2), 139–165 (2015)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Jones, T.H., Song, I.Y.: Analysis of binary/ternary cardinality combinations in entity-relationship modeling. Data Knowl. Eng. 19(1), 39–64 (1996)CrossRefGoogle Scholar
  9. 9.
    Langeveldt, W., Link, S.: Empirical evidence for the usefulness of Armstrong relations in the acquisition of meaningful functional dependencies. Inf. Syst. 35(3), 352–374 (2010)CrossRefGoogle Scholar
  10. 10.
    Liddle, S.W., Embley, D.W., Woodfield, S.N.: Cardinality constraints in semantic data models. Data Knowl. Eng. 11(3), 235–270 (1993)CrossRefGoogle Scholar
  11. 11.
    McAllister, A.J.: Complete rules for \(n\)-ary relationship cardinality constraints. Data Knowl. Eng. 27(3), 255–288 (1998)CrossRefGoogle Scholar
  12. 12.
    Queralt, A., Artale, A., Calvanese, D., Teniente, E.: OCL-Lite: finite reasoning on UML/OCL conceptual schemas. Data Knowl. Eng. 73, 1–22 (2012)CrossRefGoogle Scholar
  13. 13.
    Roblot, T.: Cardinality constraints for probabilistic and possibilistic databases. Ph.D. thesis, Department of Computer Science, The University of Auckland (2016)Google Scholar
  14. 14.
    Roblot, T., Link, S.: Probabilistic cardinality constraints. In: Johannesson, P., Lee, M.L., Liddle, S.W., Opdahl, A.L., López, Ó.P. (eds.) ER 2015. LNCS, vol. 9381, pp. 214–228. Springer, Cham (2015). doi: 10.1007/978-3-319-25264-3_16CrossRefGoogle Scholar
  15. 15.
    Roblot, T.K., Link, S.: URD: a data summarization tool for the acquisition of meaningful cardinality constraints with probabilistic intervals. In: 33rd IEEE International Conference on Data Engineering, ICDE 2017, San Diego, CA, USA, 19–22 April 2017, pp. 1379–1380. IEEE Computer Society (2017)Google Scholar
  16. 16.
    Saha, B., Srivastava, D.: Data quality: the other face of big data. In: Cruz, I.F., Ferrari, E., Tao, Y., Bertino, E., Trajcevski, G. (eds.) IEEE 30th International Conference on Data Engineering, ICDE 2014, Chicago, IL, USA, March 31–April 4 2014, pp. 1294–1297. IEEE Computer Society (2014)Google Scholar
  17. 17.
    Suciu, D., Olteanu, D., Ré, C., Koch, C.: Probabilistic Databases. Synthesis Lectures on Data Management. Morgan & Claypool Publishers, San Rafael (2011)CrossRefGoogle Scholar
  18. 18.
    Thalheim, B.: Fundamentals of cardinality constraints. In: Pernul, G., Tjoa, A.M. (eds.) ER 1992. LNCS, vol. 645, pp. 7–23. Springer, Heidelberg (1992). doi: 10.1007/3-540-56023-8_3CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of AucklandAucklandNew Zealand

Personalised recommendations