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International Workshop on Information Search, Integration, and Personalization

ISIP 2016: Information Search, Integration, and Personlization pp 18–32Cite as

Discovering Injective Mapping Between Relations in Astrophysics Databases

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 760))

Abstract

Data in Astrophysics are very often structured with the relational data model. One particularity is that every value is a real number and comes with an associated error measure, leading to a numerical interval \([value - error, value + error]\). Such Astrophysics databases can be seen as interval-based numerical databases.

Classical data mining approach, specifically those related to integrity constraints, are likely to produce useless results on such databases, as the strict equality is very unlikely to give meaningful results.

In this paper, we revisit a well-known problem, based on unary inclusion dependency discovery, to match the particularities of Astrophysics Databases. We propose to discover injective mapping between attributes of a source relation and a target relation. At first, we define two notions of inclusion between intervals. Then, we adapt a condensed representation proposed in [15] allowing to find a mapping function between the source and the target. The proposition has been implemented and several experiments have been conducted on both real-life and synthetic databases.

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References

  1. Ahuja, R.K., Magnanti, T.L., Orlin, J.B.: Network Flows: Theory, Algorithms, and Applications. Prentice-Hall Inc., Upper Saddle River (1993)

    MATH  Google Scholar 

  2. Bertsekas, D.P.: A distributed algorithm for the assignment problem (1979)

    Google Scholar 

  3. Dantzig, G.B.: Origins of the simplex method. In: Dantzig, G.B. (ed.) A History of Scientific Computing, pp. 141–151. ACM, New York (1990)

    Google Scholar 

  4. Dawes, M.: The optimal assignment problem (2012)

    Google Scholar 

  5. Diallo, T., Petit, J.-M., Servigne, S.: Discovering editing rules for data cleaning. In: 10th International Workshop on Quality in Databases in Conjunction with VLDB (Very Large Databases), pp. 1–8, August 2012

    Google Scholar 

  6. Fan, W., Geerts, F., Li, J., Xiong, M.: Discovering conditional functional dependencies. IEEE Trans. Knowl. Data Eng. 23(5), 683–698 (2011)

    Article  Google Scholar 

  7. Fan, W., Li, J., Ma, S., Tang, N., Yu, W.: Towards certain fixes with editing rules and master data. VLDB J. 21(2), 213–238 (2012)

    Article  Google Scholar 

  8. Golin, M.J.: Bipartite matching and the Hungarian method (2006)

    Google Scholar 

  9. Khuller, S.: Design and analysis of algorithms: Course notes (1998)

    Google Scholar 

  10. Klein, M.: A primal method for minimal cost flows with applications to the assignment and transportation problems. Manage. Sci. 14(3), 205–220 (1967)

    Article  Google Scholar 

  11. Kuhn, H.W.: The Hungarian method for the assignment problem. Naval Res. Logist. Q. 2, 83–97 (1955)

    Article  MathSciNet  Google Scholar 

  12. Kuhn, H.W.: Variants of the Hungarian method for assignment problems. Naval Res. Logist. Q. 3, 253–258 (1956)

    Article  MathSciNet  Google Scholar 

  13. Lawler, E.: Combinatorial Optimization: Networks and Matroids. Dover Books on Mathematics Series Mineola. Dover Publications, Mineola (2001)

    Google Scholar 

  14. Levene, M., Loizou, G.: A Guided Tour of Relational Databases and Beyond. Springer, London (1999). doi:10.1007/978-0-85729-349-7

    Book  MATH  Google Scholar 

  15. Marchi, F., Lopes, S., Petit, J.-M.: Efficient algorithms for mining inclusion dependencies. In: Jensen, C.S., Šaltenis, S., Jeffery, K.G., Pokorny, J., Bertino, E., Böhn, K., Jarke, M. (eds.) EDBT 2002. LNCS, vol. 2287, pp. 464–476. Springer, Heidelberg (2002). doi:10.1007/3-540-45876-X_30

    Chapter  Google Scholar 

  16. Papenbrock, T., Bergmann, T., Finke, M., Zwiener, J., Naumann, F.: Data profiling with metanome. PVLDB 8(12), 1860–1863 (2015)

    Google Scholar 

  17. Snodgrass, R.T.: The temporal query language TQUEL. ACM Trans. Database Syst. 12(2), 247–298 (1987)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgments

This work has been partially funded by the CNRS Mastodons projects (QualiSky 2016 and 2017).

Author information

Authors and Affiliations

  1. Université de Lyon, CNRS, INSA-Lyon, LIRIS, UMR5205, 69621, Villeurbanne, France

    Nicu-Razvan Stancioiu, Jean-Marc Petit & Vasile-Marian Scuturici

  2. Clermont-Université, Université Blaise Pascal, LIMOS, CNRS, Clermont-Ferrand, France

    Lhouari Nourine

  3. Clermont-Université, LPC, CNRS, Clermont-Ferrand, France

    Emmanuel Gangler & Philippe Gris

  4. IN2P3, CNRS, Marseile, France

    Dominique Fouchez

Authors
  1. Nicu-Razvan Stancioiu
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  2. Lhouari Nourine
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  3. Jean-Marc Petit
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  4. Vasile-Marian Scuturici
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  5. Dominique Fouchez
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  6. Emmanuel Gangler
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  7. Philippe Gris
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Corresponding author

Correspondence to Jean-Marc Petit .

Editor information

Editors and Affiliations

  1. Saint Lab ETIS, Université de Cergy-Pontoise, Pontoise, France

    Dimitris Kotzinos

  2. Laboratoire ETIS, Université de Cergy Pontoise, Cergy Pontoise, France

    Dominique Laurent

  3. Dept Informatique INSA Lyon, Université de Lyon, Villeurbanne Cedex, France

    Jean-Marc Petit

  4. LRI, University of Paris, Orsay, France

    Nicolas Spyratos

  5. Graduate School of Information Science Knowledge Media Laboratory, Hokkaido University, Sapporo, Japan

    Yuzuru Tanaka

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Stancioiu, NR. et al. (2017). Discovering Injective Mapping Between Relations in Astrophysics Databases. In: Kotzinos, D., Laurent, D., Petit, JM., Spyratos, N., Tanaka, Y. (eds) Information Search, Integration, and Personlization. ISIP 2016. Communications in Computer and Information Science, vol 760. Springer, Cham. https://doi.org/10.1007/978-3-319-68282-2_2

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  • DOI: https://doi.org/10.1007/978-3-319-68282-2_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68281-5

  • Online ISBN: 978-3-319-68282-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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