Skip to main content

Scalagon: An Efficient Skyline Algorithm for All Seasons

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9050))

Abstract

Skyline queries are well-known in the database community and there are many algorithms for the computation of the Pareto frontier. The most prominent algorithms are based on a block-nested-loop style tuple-to-tuple comparison (BNL). Another approach exploits the lattice structure induced by a Skyline query over low-cardinality domains. In this paper, we present Scalagon, an algorithm which combines the ideas of the lattice approach and a BNL-style algorithm to evaluate Skylines on arbitrary domains. Since multicore processors are going mainstream, we also present a parallel version of Scalagon. We demonstrate through extensive experimentation on synthetic and real datasets that our algorithm can result in a significant performance advantage over existing techniques.

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

Buying options

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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Börzsönyi, S., Kossmann, D., Stocker, K.: The Skyline Operator. In: ICDE 2001 Proceedings of the 17th International Conference on Data Engineering, pp. 421–430. IEEE Computer Society, Washington, DC (2001)

    Google Scholar 

  2. Chomicki, J., Ciaccia, P., Meneghetti, N.: Skyline Queries, Front and Back. SIGMOD Rec. 42(3), 6–18 (2013)

    Article  Google Scholar 

  3. Kossmann, D., Ramsak, F., Rost, S.: Shooting stars in the sky: an online algorithm for skyline queries. In: VLDB 2002 Proceedings of the 28th International Conference on Very Large Data Bases, pp. 275–286. VLDB Endowment (2002)

    Google Scholar 

  4. Chomicki, J., Godfrey, P., Gryz, J., Liang, D.: Skyline with presorting. In: ICDE 2003 Proceedings of the 19th International Conference on Data Engineering, pp. 717–816 (2003)

    Google Scholar 

  5. Godfrey, P., Shipley, R., Gryz, J.: Algorithms and Analyses for Maximal Vector Computation. The VLDB Journal 16(1), 5–28 (2007)

    Article  Google Scholar 

  6. Balke, W.-T., Güntzer, U., Zheng, J.X.: Efficient distributed skylining for web information systems. In: Bertino, E., Christodoulakis, S., Plexousakis, D., Christophides, V., Koubarakis, M., Böhm, K. (eds.) EDBT 2004. LNCS, vol. 2992, pp. 256–273. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  7. Wu, P., Zhang, C., Feng, Y., Zhao, B.Y., Agrawal, D.P., El Abbadi, A.: Parallelizing skyline queries for scalable distribution. 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. 112–130. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  8. Cosgaya-Lozano, A., Rau-Chaplin, A., Zeh, N.: Parallel computation of skyline queries. In: HPCS 2007 Proceedings of the 21st International Symposium on High Performance Computing Systems and Applications, p. 12. IEEE Computer Society, Washington, DC (2007)

    Google Scholar 

  9. Rocha-Junior, J.B., Vlachou, A., Doulkeridis, C., Nørvåg, K.: AGiDS: a grid-based strategy for distributed skyline query processing. In: Hameurlain, A., Tjoa, A.M. (eds.) Globe 2009. LNCS, vol. 5697, pp. 12–23. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  10. Gao, Y., Chen, G.-C., Chen, L., Chen, C.: Parallelizing progressive computation for skyline queries in multi-disk environment. In: Bressan, S., Küng, J., Wagner, R. (eds.) DEXA 2006. LNCS, vol. 4080, pp. 697–706. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  11. Park, S., Kim, T., Park, J., Kim, J., Im, H.: Parallel Skyline Computation on Multicore Architectures. In: ICDE 2009 Proceedings of the 2009 IEEE International Conference on Data Engineering, pp. 760–771. IEEE Computer Society, Washington, DC (2009)

    Google Scholar 

  12. Selke, J., Lofi, C., Balke, W.-T.: Highly scalable multiprocessing algorithms for preference-based database retrieval. In: Kitagawa, H., Ishikawa, Y., Li, Q., Watanabe, C. (eds.) DASFAA 2010. LNCS, vol. 5982, pp. 246–260. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  13. Afrati, F.N., Koutris, P., Suciu, D., Ullman, J.D.: Parallel skyline queries. In: ICDT 2012 Proceedings of the 15th International Conference on Database Theory, pp. 274–284. ACM, New York (2012)

    Google Scholar 

  14. Liknes, S., Vlachou, A., Doulkeridis, C., Nørvåg, K.: APSkyline: improved skyline computation for multicore architectures. In: Bhowmick, S.S., Dyreson, C.E., Jensen, C.S., Lee, M.L., Muliantara, A., Thalheim, B. (eds.) DASFAA 2014, Part I. LNCS, vol. 8421, pp. 312–326. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  15. Endres, M., Kießling, W.: High parallel skyline computation over low-cardinality domains. In: Manolopoulos, Y., Trajcevski, G., Kon-Popovska, M. (eds.) ADBIS 2014. LNCS, vol. 8716, pp. 97–111. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  16. Tan, K.-L., Eng, P.-K., Ooi, B.C.: Efficient progressive skyline computation. In: VLDB 2001 Proceedings of the 27th International Conference on Very Large Data Bases, pp. 301–310. Morgan Kaufmann Publishers Inc, San Francisco (2001)

    Google Scholar 

  17. Papadias, D., Tao, Y., Fu, G., Seeger, B.: An optimal and progressive algorithm for skyline queries. In: SIGMOD 2003 Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, pp. 467–478. ACM, New York (2003)

    Google Scholar 

  18. Lee, K., Zheng, B., Li, H., Lee, W.-C.: Approaching the skyline in Z order. In: VLDB 2007 Proceedings of the 33rd International Conference on Very Large Data Bases, pp. 279–290. VLDB Endowment (2007)

    Google Scholar 

  19. Morse, M., Patel, J.M., Jagadish, H.V.: Efficient skyline computation over low-cardinality domains. In: VLDB 2007 Proceedings of the 33rd International Conference on Very Large Data Bases, pp. 267–278. VLDB Endowment (2007)

    Google Scholar 

  20. Preisinger, T., Kießling, W.: The hexagon algorithm for evaluating pareto preference queries. In: MPref 2007 Proceedings of the 3rd Multidisciplinary Workshop on Advances in Preference Handling (in conjunction with VLDB 2007) (2007)

    Google Scholar 

  21. Lee, J., Hwang, S.-W.: BSkyTree: scalable skyline computation using a balanced pivot selection. In: EDBT 2010 Proceedings of the 13th International Conference on Extending Database Technology, pp. 195–206. ACM, New York (2010)

    Google Scholar 

  22. Davey, B.A., Priestley, H.A.: Introduction to Lattices and Order, 2nd edn. Cambridge University Press, Cambridge (2002)

    Book  MATH  Google Scholar 

  23. Aggarwal, C.C., Yu, P.S.: Outlier detection for high dimensional data. In: SIGMOD 2001 Proceedings of the 2001 ACM SIGMOD International Conference on Management of Data, vol. 30, pp. 37–46. ACM, New York, May 2001

    Google Scholar 

  24. Aggarwal, C.C.: Outlier Analysis. Springer, New York (2013)

    Book  MATH  Google Scholar 

  25. Chan, C.-Y., Jagadish, H.V., Tan, K.-L., Tung, A.K.H., Zhang, Z.: On high dimensional skylines. 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. 478–495. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  26. Bentley, J.L.: Programming Pearls. Addison-Wesley (2000)

    Google Scholar 

  27. Roocks, P.: R script for \(\alpha \) determination (2014). http://www.informatik.uni-augsburg.de/en/chairs/dbis/db/staff/roocks/publications/rpref_alpha.zip

  28. Shang, H., Kitsuregawa, M.: Skyline operator on anti-correlated distributions. In: VLDB 2013 Proceedings of the 39rd International Conference on Very Large Data Bases, vol. 6, pp. 649–660 (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Markus Endres .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Endres, M., Roocks, P., Kießling, W. (2015). Scalagon: An Efficient Skyline Algorithm for All Seasons. In: Renz, M., Shahabi, C., Zhou, X., Cheema, M. (eds) Database Systems for Advanced Applications. DASFAA 2015. Lecture Notes in Computer Science(), vol 9050. Springer, Cham. https://doi.org/10.1007/978-3-319-18123-3_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-18123-3_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18122-6

  • Online ISBN: 978-3-319-18123-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics