Improving Source Selection in Large Scale Mediation Systems through Combinatorial Optimization Techniques

  • Alexandra Pomares
  • Claudia Roncancio
  • Van-Dat Cung
  • María-del-Pilar Villamil
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6790)


This paper concerns querying in large scale virtual organizations. Such organizations are characterized by a challenging data context involving a large number of distributed data sources with strong heterogeneity and uncontrolled data overlapping. In that context, data source selection during query evaluation is particularly important and complex. To cope with this task, we propose OptiSource, an original strategy for source selection using combinatorial optimization techniques combined to organizational knowledge of the virtual organization. Experiment numerical results show that OptiSource is a robust strategy that improves the precision and the recall of the source selection process. This paper presents the data and knowledge models, the definition of OptiSource, the related mathematical model, the prototype and an extensive experimental study.


Large Scale Data Mediation Source Selection Combinatorial Optimization 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Foster, I., Kesselman, C., Tuecke, S.: The anatomy of the grid: Enabling scalable virtual organizations. International Journal of High Performance Computing Applications 15, 200–222 (2001)CrossRefGoogle Scholar
  2. 2.
    NEESGrid: Nees consortium (2008),
  3. 3.
    BIRN: Bioinformatics research network (2008),
  4. 4.
    Quiané-Ruiz, J.-A., Lamarre, P., Valduriez, P.: Sqlb: A query allocation framework for autonomous consumers and providers. In: VLDB, pp. 974–985 (2007)Google Scholar
  5. 5.
    Doan, A., Halevy, A.Y.: Efficiently ordering query plans for data integration. In: ICDE 2002, p. 393. IEEE Computer Society, Washington, DC, USA (2002)Google Scholar
  6. 6.
    Wolf, G., Khatri, H., Chokshi, B., Fan, J., Chen, Y., Kambhampati, S.: Query processing over incomplete autonomous databases. In: VLDB, pp. 651–662 (2007)Google Scholar
  7. 7.
    Huebsch, R., Hellerstein, J.M., Lanham, N., Loo, B.T., Shenker, S., Stoica, I.: Querying the internet with pier. In: VLDB, Berlin, Germany, pp. 321–332 (2003)Google Scholar
  8. 8.
    Pottinger, R., Halevy, A.Y.: Minicon: A scalable algorithm for answering queries using views. VLDB Journal. 10(2-3), 182–198 (2001)zbMATHGoogle Scholar
  9. 9.
    Pomares, A., Roncancio, C., Cung, V.-D., Abásolo, J., Villamil, M.-d.-P.: Source selection in large scale data contexts: An optimization approach. In: Bringas, P.G., Hameurlain, A., Quirchmayr, G. (eds.) DEXA 2010. LNCS, vol. 6261, pp. 46–61. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Levy, A.Y., Rajaraman, A., Ordille, J.J.: Querying heterogeneous information sources using source descriptions. In: VLDB, pp. 251–262 (1996)Google Scholar
  11. 11.
    Garcia-Molina, H., Papakonstantinou, Y., Quass, D., Rajaraman, A., Sagiv, Y., Ullman, J.D., Vassalos, V., Widom, J.: The tsimmis approach to mediation: Data models and languages. Journal of Intelligent Information Systems 8, 117–132 (1997)CrossRefGoogle Scholar
  12. 12.
    Tomasic, A., Raschid, L., Valduriez, P.: Scaling access to heterogeneous data sources with DISCO. Knowledge and Data Engineering 10, 808–823 (1998)CrossRefGoogle Scholar
  13. 13.
    Yerneni, R.: Mediated Query Processing Over Autonomous Data Sources. PhD thesis, Stanford University, Stanford, CA (2001)Google Scholar
  14. 14.
    Bleiholder, J., Khuller, S., Naumann, F., Raschid, L., Wu, Y.: Query planning in the presence of overlapping sources. In: Ioannidis, Y., Scholl, M.H., Schmidt, J.W., Matthes, F., Hatzopoulos, M., Böhm, K., Kemper, A., Grust, T., Böhm, C. (eds.) EDBT 2006. LNCS, vol. 3896, pp. 811–828. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  15. 15.
    Naumann, F., Freytag, J.C., Leser, U.: Completeness of integrated information sources. Information Systems Journal, Special issue: Data Quality in Cooperative Information Systems 29, 583–615 (2004)Google Scholar
  16. 16.
    Tatarinov, I., Ives, Z., Madhavan, J., Halevy, A., Suciu, D., Dalvi, N., Dong, X(L.), Kadiyska, Y., Miklau, G., Mork, P.: The piazza peer data management project. SIGMOD Rec. 32(3), 47–52 (2003)CrossRefGoogle Scholar
  17. 17.
    Nejdl, W., Wolf, B., Qu, C., Decker, S., Sintek, M., Naeve, A., Nilsson, M., Palmér, M., Risch, T.: Edutella: a p2p networking infrastructure based on rdf. In: WWW 2002, pp. 604–615. ACM, New York (2002)Google Scholar
  18. 18.
    Adjiman, P., Goasdoué, F., Rousset, M.-C.: Somerdfs in the semantic web. Journal on Data Semantics 8, 158–181 (2007)zbMATHGoogle Scholar
  19. 19.
    Horrocks, I.: Owl: A description logic based ontology language. In: Principles and Practice of Constraint Programming, pp. 5–8 (2005)Google Scholar
  20. 20.
    Pomares, A., Roncancio, C., Abasolo, J., del Pilar Villamil, M.: Knowledge based query processing. In: ICEIS. Lecture Notes in Business Information Processing, vol. 24, pp. 208–219. Springer, Heidelberg (2009)Google Scholar
  21. 21.
    Hillier, F.S., Lieberman, G.J.: Introduction to Operations Research, 8th edn. McGraw-Hill, New York (2005)zbMATHGoogle Scholar
  22. 22.
    Makhorin, A.: Gnu project, gnu linear programming kit (2009),
  23. 23.
    Makhorin, A.: Gnu project, glpk for java (2009),
  24. 24.
    Eric Prud, A.S.: Sparql query language for rdf (2007),
  25. 25.
    Lin, C.-J., Wen, U.-P.: Sensitivity analysis of the optimal assignment. European Journal of Operational Research 149(1), 35–46 (2003)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Alexandra Pomares
    • 1
  • Claudia Roncancio
    • 2
  • Van-Dat Cung
    • 2
  • María-del-Pilar Villamil
    • 3
  1. 1.Pontificia Universidad JaverianaBogotáColombia
  2. 2.Grenoble INPGrenobleFrance
  3. 3.Universidad de los AndesBogotáColombia

Personalised recommendations