Skip to main content
SpringerLink
Log in

Efficient Selection of Various k-Objects for a Keyword Query Based on MapReduce Skyline Algorithm

  • Conference paper
Databases in Networked Information Systems (DNIS 2014)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8381))

Included in the following conference series:

Abstract

Recently, keyword-based query interface is a de facto standard for information retrieval. A user gives a keyword and gets necessary objects that are closely related to the keyword. How to select the necessary objects is one of the most important problem in database literature. Top-k query is popular method to select important objects from large candidate objects. A user specfies a scoring function and k. Then, the top-k query selects the k objects based on the scoring function. However, each user may have different scoring function to select the top-k object, which means the top-k objects are valuable only for users who share the same scoring function. In this paper, we propose k-objects selection function that selects various k objects that are preferable for all users who may have different scoring function. We applied the idea of skyline queries to select the k objects in this paper. We also considered efficient computation by using MapReduce flamework.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Fagin, R., Lotem, A., Naor, M.: Optimal aggregation algorithms for middleware. In: Proceedings of ACM PODS, pp. 102–113 (2001)

    Google Scholar 

  2. Bruno, N., Gravano, L., Marian, A.: Evaluating top-k queries over web-accessible databases. ACM Transactions on Database Systems 29(2), 319–362 (2004)

    Article  Google Scholar 

  3. Chang, K.C., Hwang, S.-W.: Minimal probing: supporting expensive predicates for top-k queries. In: Proceedings of ACM SIGMOD, pp. 346–357 (2002)

    Google Scholar 

  4. Hwang, S.-W., Chang, K.C.: Optimizing access cost for top-k queries over web sources. In: Proceedings of IEEE ICDE, pp. 188–189 (2005)

    Google Scholar 

  5. Bentley, J.L., Kung, H.T., Schkolnick, M., Thompson, C.D.: On the average number of maxima in a set of vectors and applications. Journal of ACM 25(4), 536–543 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  6. Bentley, J.L., Clarkson, K.L., Levine, D.B.: Fast linear expected-time algorithms for computing maxima and convex hulls. In: Proceedings of ACM-SIAM SODA, pp. 179–187 (1990)

    Google Scholar 

  7. Nielsen, O.B., Sobel, M.: On the distribution of the number of admissable points in a vector random sample. Theory of Probability and its Application 11(2), 249–269 (1966)

    Article  MATH  Google Scholar 

  8. Lin, X., Yuan, Y., Zhang, Q., Zhang, Y.: Selecting stars: the k most representative skyline operator. In: Proceedings of IEEE ICDE, pp. 86–95 (2007)

    Google Scholar 

  9. Papadias, D., Tao, Y., Fu, G., Seeger, B.: Progressive skyline computation in database systems. ACM Transactions on Database Systems 30(1), 41–82 (2005)

    Article  Google Scholar 

  10. Tao, Y., Ding, L., Lin, X., Pei, J.: Distance-based representative skyline. In: Proceedings of IEEE ICDE, pp. 892–903 (2009)

    Google Scholar 

  11. Borzsonyi, S., Kossmann, D., Stocker, K.: The skyline operator. In: Proceedings of IEEE ICDE, pp. 421–430 (2001)

    Google Scholar 

  12. Kossmann, D., Ramsak, F., Rost, S.: Shooting stars in the sky: An online algorithm for skyline queries. In: Proceedings of VLDB, pp. 275–286 (2002)

    Google Scholar 

  13. Chomicki, J., Godfrey, P., Gryz, J., Liang, D.: Skyline with Presorting. In: Proceedings of IEEE ICDE, pp. 717–719 (2003)

    Google Scholar 

  14. Tan, K.-L., Eng, P.-K., Ooi, B.C.: Efficient Progressive Skyline Computation. In: Proceedings of VLDB, pp. 301–310 (2001)

    Google Scholar 

  15. Vlachou, A., Doulkeridis, C., Kotidis, Y., Vazirgiannis, M.: SKYPEER: Efficient Subspace Skyline Computation over Distributed Data. In: Proceedings of IEEE ICDE, pp. 416–425 (2007)

    Google Scholar 

  16. Fotiadou, K., Pitoura, E.: BITPEER: Continuous Subspace Skyline Computation with Distributed Bitmap Indexes. In: Proceedings of DaMaP, pp. 35–42 (2008)

    Google Scholar 

  17. Chan, C.-Y., Jagadish, H.V., Tan, K.-L., Tung, A.K.H., Zhang, Z.: On High Dimensional Skylines. In: Ioannidis, Y., et al. (eds.) EDBT 2006. LNCS, vol. 3896, pp. 478–495. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  18. Tao, Y., Xiao, X., Pei, J.: Subsky: Efficient Computation of Skylines in Subspaces. In: Proceedings of IEEE ICDE, pp. 65–65 (2006)

    Google Scholar 

  19. Tao, Y., Lin, W.: XIAO, X.: Minimal MapReduce Algorithm. In: Proceedings of ACM SIGMOD, pp. 529–540 (2013)

    Google Scholar 

  20. Park, Y., Min, J, Shim, K.: Parallel Computation of Skyline and Reverse Skyline Queries Using MapReduce. In: Proceedings of VLDB, pp. 2002–2013 (2013)

    Google Scholar 

  21. Jiang, D., Tung, A.K.H., Chen, G.: MAP-JOIN-REDUCE: Toward Scalable and Efficient Data Analysis on Large Clusters. IEEE Transactions on Knowledge and Data Engineering 23(9), 1299–1311 (2011)

    Article  Google Scholar 

  22. Blanas, S., Patel, J.M., Ercegovac, V., Rao, J., Shekita, E.J., Tian, Y.: A comparison of join algorithms for log processing in MaPreduce. In: Proceedings of ACM SIGMOD, pp. 975–986 (2010)

    Google Scholar 

  23. Vernica, R., Carey, M.J., Li, C.: Efficient parallel set-similarity joins using MapReduce. In: Proceedings of ACM SIGMOD, pp. 495–506 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Hiroshima University, 1-7-1 Kagamiyama, Higashi-Hiroshima, 739-8521, Japan

    Md. Anisuzzaman Siddique & Yasuhiko Morimoto

Authors
  1. Md. Anisuzzaman Siddique
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yasuhiko Morimoto
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University of Aizu, Aizu Wakamatsu Shi, 965-8580, Fukushima Ken, Japan

    Aastha Madaan

  2. University of Aizu, Aizu Wakamatsu, 965-8580, Fukushima, Japan

    Shinji Kikuchi

  3. Graduate Department of Computer and Information Systems, University of Aizu, Ikki Machi, Aizu-Wakamatsu, 965-8580, Fukushima, Japan

    Subhash Bhalla

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Siddique, M.A., Morimoto, Y. (2014). Efficient Selection of Various k-Objects for a Keyword Query Based on MapReduce Skyline Algorithm. In: Madaan, A., Kikuchi, S., Bhalla, S. (eds) Databases in Networked Information Systems. DNIS 2014. Lecture Notes in Computer Science, vol 8381. Springer, Cham. https://doi.org/10.1007/978-3-319-05693-7_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-05693-7_3

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-05692-0

  • Online ISBN: 978-3-319-05693-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation