Advertisement

Journal of Intelligent Information Systems

, Volume 40, Issue 3, pp 479–500 | Cite as

Provisional reporting for rank joins

  • Adnan Abid
  • Marco Tagliasacchi
Article

Abstract

Rank join operators perform a relational join among two or more relations, assign numeric scores to the join results based on a given scoring function, and return K join results with the highest scores, while accessing a subset of data from the input relations. Most of the rank join operators compute a score upper bound for a join result that can be potentially obtained after retrieving the unseen data. A join result is kept in an output buffer, and is deterministically reported to the user if its score is greater than or equal to the score upper bound. The value of the score upper bound decreases subject to further data extraction from the relations. In case of Web services as data sources, which are characterized by non-negligible response time for every data fetch, the value of score upper bound might decrease slowly. Consequently, there is a long delay in reporting a join result stored in the output buffer. This paper addresses the problem of efficiently reporting a top join result obtained by joining the data of two Web services, which are characterized by non-negligible response time. We present a probabilistic reporting method which computes the confidence with which a join result may appear among final top-K joins. It reports a join result as soon as the measure of its confidence exceeds a given threshold. This helps in reporting a join result soon after its observation. An extensive experimental study with various settings of different operating parameters validates the importance of the proposed approach on both real and synthetic data sets. The results show that our proposed approach significantly reduces the average difference between the time when a join result is observed and the time when it is reported, while incurring negligible errors in the final results.

Keywords

Rank joins Top-K queries Probabilistic reporting Ranking in web services 

Notes

Acknowledgements

This research is part of the “Search Computing” (SeCo) project, funded by the European Research Council (ERC), under the 2008 Call for “IDEAS Advanced Grants”, dedicated to frontier research. We are thankful to Prof. Stefano Ceri for his guidance and useful discussions during this work.

References

  1. Abid, A., & Tagliasacchi, M. (2011). Parallel data access for multiway rank joins. In ICWE conference (pp. 44–58).Google Scholar
  2. Arai, B., Das, G., Gunopulos, D., Koudas, N. (2007). Anytime measures for top-k algorithms. In Proceedings of the 33rd international conference on very large data bases (VLDB ’07) VLDB endowment (pp. 914–925).Google Scholar
  3. Arai, B., Das, G., Gunopulos, D., Koudas, N. (2009). Anytime measures for top-k algorithms on exact and fuzzy data sets. The VLDB Journal, 18, 407–427.CrossRefGoogle Scholar
  4. Barbará, D., Garcia-Molina, H., Porter, D. (1992). The management of probabilistic data. IEEE Transactions on Knowledge and Data Engineering, 4(5), 487–502.CrossRefGoogle Scholar
  5. Braga, D., Ceri, S., Daniel, F., Martinenghi, D. (2008). Mashing up search services. IEEE Internet Computing, 12(5), 16–23.CrossRefGoogle Scholar
  6. Bruno, N., Chaudhuri, S., Gravano, L. (2002). Top-k selection queries over relational databases: mapping strategies and performance evaluation. ACM Transactions on Database Systems, 27, 153–187.CrossRefGoogle Scholar
  7. Bruno, N., Gravano, L., Marian, A. (2002). Evaluating top-k queries over web-accessible databases. In ICDE (p. 369).Google Scholar
  8. Ceri, S. (2010). Search Computing: Challenges and Directions. Lecture Notes in Computer Science.Google Scholar
  9. Chang, K.C.-C., & Hwang, S.-W. (2002). Minimal probing: supporting expensive predicates for top-k queries. In Proceedings of the 2002 ACM SIGMOD international conference on management of data, (SIGMOD ’02) (pp. 346–357). New York, USA: ACM Press.Google Scholar
  10. Cheng, R., Kalashnikov, D.V., Prabhakar, S. (2003). Evaluating probabilistic queries over imprecise data. In SIGMOD conference (pp. 551–562).Google Scholar
  11. Dalvi, C.R.N., & Suciu, D. (2007). Efficient top-k query evaluation on probabilistic data. In ICDE (pp. 886–895).Google Scholar
  12. Fagin, R. (1998). Fuzzy queries in multimedia database systems. In Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on principles of database systems (pp. 1–10). Seattle, Washington: ACM Press.CrossRefGoogle Scholar
  13. Fagin, R. (1999). Combining fuzzy information from multiple systems. Journal of Computer and System Sciences, 58(1), 83–99.MathSciNetMATHCrossRefGoogle Scholar
  14. Fagin, R., & Wimmers, E.L. (1997). Incorporating user preferences in multimedia queries. In Proceedings of the 6th international conference on database theory (pp. 247–261). London, UK: Springer-Verlag.Google Scholar
  15. Horvitz, E.J. (1987). Reasoning about beliefs and actions under computational resource constraints. In Proceedings of the 1987 workshop on uncertainty in artificial intelligence (pp. 429–444).Google Scholar
  16. Hua, M., Pei, J., Zhang, W., Lin, X. (2008). Efficiently answering probabilistic threshold top-k queries on uncertain data. In Proceedings of the 2008 IEEE 24th international conference on data engineering (pp. 1403–1405). Washington, DC, USA: IEEE Computer Society.CrossRefGoogle Scholar
  17. Ilyas, I., Aref, W., Elmagarmid, A. (2004). Supporting top-k join queries in relational databases. The VLDB Journal, 13(3), 207–221.CrossRefGoogle Scholar
  18. Lakshmanan, L.V.S., Leone, N., Ross, R., Subrahmanian, V.S. (1997). Probview: a flexible probabilistic database system. ACM Transactions on Database Systems, 22, 419–469.CrossRefGoogle Scholar
  19. Lian, X., & Chen, L. (2008). Probabilistic ranked queries in uncertain databases. In Proceedings of the 11th international conference on extending database technology: Advances in database technology (EDBT ’08) (pp. 511–522). New York, USA: ACM Press.CrossRefGoogle Scholar
  20. Marian, A., Bruno, N., Gravano, L. (2004). Evaluating top-k queries over web-accessible databases. ACM Transactions on Database Systems, 29(2), 319–362.CrossRefGoogle Scholar
  21. Natsev, A., chi Chang, Y., Smith, J.R., Li, C.-S., Vitter, J.S. (2001). Supporting incremental join queries on ranked inputs. In VLDB conference (pp. 281–290).Google Scholar
  22. Ślezak, D., & Kowalski, M. (2010). Towards approximate sql: infobright’s approach. In Proceedings of the 7th international conference on rough sets and current trends in computing (RSCTC’10) (pp. 630–639). Berlin, Heidelberg: Springer-Verlag.Google Scholar
  23. Soliman, M.A., & Ilyas, I.F. (2007). Top-k query processing in uncertain databases. In ICDE (pp. 896–905).Google Scholar
  24. Theobald, M., Weikum, G., Schenkel, R. (2004). Top-k query evaluation with probabilistic guarantees. In Proceedings of the thirtieth international conference on very large data bases, VLDB endowment (VLDB ’04) (Vol. 30, pp. 648–659). .Google Scholar
  25. Yi, K., Li, F., Kollios, G., Srivastava, D. (2008). Efficient processing of top-k queries in uncertain databases. In ICDE (pp. 1406–1408).Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Dipartimento di Elettronica e InformazionePolitecnico di MilanoMilanoItaly
  2. 2.Faculty of Information TechnologyUniversity of Central PunjabLahorePakistan

Personalised recommendations