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An Efficient Initialization Method for Probabilistic Relational Databases

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Database and Expert Systems Applications (DEXA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9828))

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Abstract

Probabilistic relational databases play an important role on uncertain data management. Informally, a probabilistic database is a probability distribution over a set of deterministic databases (namely, possible worlds). The existing initialization methods that transform the possible worlds representation into our chosen representation, make the formulae of tuples very long. An efficient initialization method is proposed by providing an equation that can generate simplified formulae of tuples. The experimental study shows that the proposed method greatly simplifies the formulae of tuples without additional time cost. The subsequent queries benefit from the simplified formulae of tuples.

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Notes

  1. 1.

    In this paper, \(A\vDash B\) means that A makes B true.

References

  1. Aggarwal, C.C.: Maybms a system for managing large probabilistic databases. Adv. Database Syst. 35, 1–34 (2009)

    Google Scholar 

  2. Aggarwal, C.C.: Trio a system for data uncertainty and lineage. Managing Min. Uncertain Data 2006, 1151–1154 (2009)

    Google Scholar 

  3. Agreste, S., De Meo, P., Ferrara, E., Ursino, D.: Xml matchers: approaches and challenges. Knowl. Based Syst. 66, 190–209 (2014)

    Article  Google Scholar 

  4. Ayat, N., Akbarinia, R., Afsarmanesh, H., Valduriez, P.: Entity resolution for probabilistic data. Inf. Sci. 277, 492–511 (2014)

    Article  MathSciNet  Google Scholar 

  5. Dalvi, N., Ré, C., Suciu, D.: Probabilistic databases: diamonds in the dirt. Commun. ACM 52(7), 86–94 (2009)

    Article  Google Scholar 

  6. Dan, O., Koch, C., Antova, L.: World-set decompositions: expressiveness and efficient algorithms. Theor. Comput. Sci. 403(2), 265–284 (2008)

    MathSciNet  MATH  Google Scholar 

  7. Fink, R., Huang, J., Olteanu, D.: Anytime approximation in probabilistic databases. VLDB J. 22(6), 823–848 (2013)

    Article  Google Scholar 

  8. Fink, R., Olteanu, D.: Dichotomies for queries with negation in probabilistic databases. ACM Trans. Database Syst. 41(1), 4–47 (2016)

    Article  Google Scholar 

  9. Fink, R., Olteanu, D., Rath, S.: Providing support for full relational algebra in probabilistic databases. In: ICDE, pp. 315–326. IEEE, Hannover, Germany (2011)

    Google Scholar 

  10. Sen, P., Deshpande, A.: Representing and querying correlated tuples in probabilistic databases. In: ICDE, pp. 596–605. IEEE, Istanbul, Turkey (2007)

    Google Scholar 

  11. Sen, P., Deshpande, A., Getoor, L.: Prdb: managing and exploiting rich correlations in probabilistic databases. VLDB J. 18(5), 1065–1090 (2009)

    Article  Google Scholar 

  12. Škrbić, S., Racković, M., Takači, A.: Prioritized fuzzy logic based information processing in relational databases. Knowl. Based Syst. 38, 62–73 (2013)

    Article  Google Scholar 

  13. Suciu, D., Olteanu, D., Ré, C., Koch, C.: Probabilistic databases. Synth. Lect. Data Manage. 3(2), 1–180 (2011)

    Article  MATH  Google Scholar 

  14. Tang, R., Cheng, R., Wu, H., Bressan, S.: A framework for conditioning uncertain relational data. In: Liddle, S.W., Schewe, K.-D., Tjoa, A.M., Zhou, X. (eds.) DEXA 2012, Part II. LNCS, vol. 7447, pp. 71–87. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

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Authors and Affiliations

  1. School of Computer Science and Technology, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, 430074, China

    Hong Zhu, Caicai Zhang & Zhongsheng Cao

Authors
  1. Hong Zhu
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  2. Caicai Zhang
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  3. Zhongsheng Cao
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Corresponding author

Correspondence to Zhongsheng Cao .

Editor information

Editors and Affiliations

  1. Clausthal University of Technology, Clausthal-Zellerfeld, Germany

    Sven Hartmann

  2. Victoria University of Wellington, Wellington, New Zealand

    Hui Ma

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Zhu, H., Zhang, C., Cao, Z. (2016). An Efficient Initialization Method for Probabilistic Relational Databases. In: Hartmann, S., Ma, H. (eds) Database and Expert Systems Applications. DEXA 2016. Lecture Notes in Computer Science(), vol 9828. Springer, Cham. https://doi.org/10.1007/978-3-319-44406-2_39

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  • DOI: https://doi.org/10.1007/978-3-319-44406-2_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44405-5

  • Online ISBN: 978-3-319-44406-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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