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Cardinality Constraints with Probabilistic Intervals

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Conceptual Modeling (ER 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10650))

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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.

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References

  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_13

    Chapter  Google Scholar 

  2. Brown, P., Link, S.: Probabilistic keys. IEEE Trans. Knowl. Data Eng. 29(3), 670–682 (2017)

    Article  Google Scholar 

  3. Chen, P.P.: The entity-relationship model - toward a unified view of data. ACM Trans. Database Syst. 1(1), 9–36 (1976)

    Article  Google Scholar 

  4. Fagin, R.: Horn clauses and database dependencies. J. ACM 29(4), 952–985 (1982)

    Article  MathSciNet  Google Scholar 

  5. Ferrarotti, F., Hartmann, S., Link, S.: Efficiency frontiers of XML cardinality constraints. Data Knowl. Eng. 87, 297–319 (2013)

    Article  Google Scholar 

  6. Hall, N., Köhler, H., Link, S., Prade, H., Zhou, X.: Cardinality constraints on qualitatively uncertain data. Data Knowl. Eng. 99, 126–150 (2015)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  10. Liddle, S.W., Embley, D.W., Woodfield, S.N.: Cardinality constraints in semantic data models. Data Knowl. Eng. 11(3), 235–270 (1993)

    Article  Google Scholar 

  11. McAllister, A.J.: Complete rules for \(n\)-ary relationship cardinality constraints. Data Knowl. Eng. 27(3), 255–288 (1998)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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_16

    Chapter  Google Scholar 

  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. 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. Suciu, D., Olteanu, D., Ré, C., Koch, C.: Probabilistic Databases. Synthesis Lectures on Data Management. Morgan & Claypool Publishers, San Rafael (2011)

    Book  Google Scholar 

  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_3

    Chapter  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Auckland, Auckland, New Zealand

    Tania Katell Roblot & Sebastian Link

Authors
  1. Tania Katell Roblot
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Correspondence to Sebastian Link .

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Editors and Affiliations

  1. University of Klagenfurt, Klagenfurt, Austria

    Heinrich C. Mayr

  2. Free University of Bozen-Bolzano, Bozen-Bolzano, Italy

    Giancarlo Guizzardi

  3. Victoria University of Wellington, Wellington, New Zealand

    Hui Ma

  4. Valencia University of Technology, Valencia, Spain

    Oscar Pastor

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Roblot, T.K., Link, S. (2017). Cardinality Constraints with Probabilistic Intervals. In: Mayr, H., Guizzardi, G., Ma, H., Pastor, O. (eds) Conceptual Modeling. ER 2017. Lecture Notes in Computer Science(), vol 10650. Springer, Cham. https://doi.org/10.1007/978-3-319-69904-2_21

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  • DOI: https://doi.org/10.1007/978-3-319-69904-2_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69903-5

  • Online ISBN: 978-3-319-69904-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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