Frontiers of Computer Science

, Volume 12, Issue 1, pp 146–162 | Cite as

Handling query skew in large indexes: a view based approach

Research Article
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Abstract

Indexing is one of the most important techniques to facilitate query processing over a multi-dimensional dataset. A commonly used strategy for such indexing is to keep the tree-structured index balanced. This strategy reduces query processing cost in the worst case, and can handle all different queries equally well. In other words, this strategy implies that all queries are uniformly issued, which is partially because the query distribution is not possibly known and will change over time in practice. A key issue we study in this work is whether it is the best to fully rely on a balanced tree-structured index in particular when datasets become larger and larger in the big data era. This means that, when a dataset becomes very large, it becomes unreasonable to assume that all data in any subspace are equally important and are uniformly accessed by all queries at the index level. Given the existence of query skew and the possible changes of query skew, in this paper, we study how to handle such query skew and such query skew changes at the index level without sacrifice of supporting any possible queries in a wellbalanced tree index and without a high overhead. To tackle the issue, we propose index-view at the index level, where an index-view is a short-cut in a balanced tree-structured index to access objects in the subspaces that are more frequently accessed, and propose a new index-view-centric framework for query processing using index-views in a bottom-up manner. We study index-views selection problem in both static and dynamic setting, and we confirm the effectiveness of our approach using large real and synthetic datasets.

Keywords

multi-dimensional index query adaptive indexview 

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Notes

Acknowledgements

This work was supported by grant of the Research Grants Council of the Hong Kong SAR, China (14209314).

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References

  1. 1.
    Guttman A. R-trees: a dynamic index structure for spatial searching. In: Proceedings of ACM Special Interest Group on Management of Data. 1984, 47–57Google Scholar
  2. 2.
    Finkel R A, Bentley J L. Quad trees: a data structure for retrieval on composite keys. Acta Informatica, 1974, 4(1): 1–9CrossRefMATHGoogle Scholar
  3. 3.
    Bentley J L. Multidimensional binary search trees used for associative searching. Communications of the ACM, 1975, 18(9): 509–517CrossRefMATHGoogle Scholar
  4. 4.
    Samet H. Foundations of Multidimensional and Metric Data Structures. San Francisco, CA: Morgan Kaufmann, 2006MATHGoogle Scholar
  5. 5.
    Silva-Filho Y V. Average case analysis of region search in balanced k-d trees. Information Processing Letters, 1979, 8(5): 219–223MathSciNetCrossRefMATHGoogle Scholar
  6. 6.
    Silverstein C, Henzinger M R, Marais H, Moricz M. Analysis of a very large web search engine query log. SIGIR Forum, 1999, 33(1): 6–12CrossRefGoogle Scholar
  7. 7.
    Gonzalez M C, Hidalgo C A, Barabasi A L. Understanding individual human mobility patterns. Nature, 2008, 453(7196): 779–782CrossRefGoogle Scholar
  8. 8.
    Yuan J, Zheng Y, Zhang C Y, Xie W L, Xie X, Sun G Z, Huang Y. Tdrive: driving directions based on taxi trajectories. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems. 2010, 99–108Google Scholar
  9. 9.
    Levandoski J J, Sarwat M, Eldawy A, Mokbel M F. Lars: a locationaware recommender system. In: Proceedings of the 28th IEEE International Conference on Data Engineering. 2012, 450–461Google Scholar
  10. 10.
    Lee R, Wakamiya S, Sumiya K. Discovery of unusual regional social activities using geo-tagged microblogs. World WideWeb, 2011, 14(4): 321–349CrossRefGoogle Scholar
  11. 11.
    Arya S, Mount D M, Netanyahu N S, Silverman R, Wu A Y. An optimal algorithm for approximate nearest neighbor searching. In: Proceedings of the 5th ACM-SIAM Symposium on Discrete Algorithms. 1994, 573–582Google Scholar
  12. 12.
    Roy S B, Chakrabarti K. Location-aware type ahead search on spatial databases: semantics and efficiency. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. 2011, 361–372Google Scholar
  13. 13.
    Friedman J H, Bentley J L, Finkel R A. An algorithm for finding best matches in logarithmic expected time. ACM Transactions on Mathematical Software, 1977, 3(3): 209–226CrossRefMATHGoogle Scholar
  14. 14.
    Papadias D, Shen QM, Tao Y F, Mouratidis K. Group nearest neighbor queries. In: Proceedings of the 20th IEEE International Conference on Data Engineering. 2004, 301–312CrossRefGoogle Scholar
  15. 15.
    Felipe I D, Hristidis V, Rishe N. Keyword search on spatial databases. In: Proceedings of the 24th IEEE International Conference on Data Engineering. 2008, 656–665Google Scholar
  16. 16.
    Cong G, Jensen C S, Wu D M. Efficient retrieval of the top-k most relevant spatial Web objects. The Proceedings of the VLDB Endowment, 2009, 2(1): 337–348CrossRefGoogle Scholar
  17. 17.
    Cao X, Cong G, Jensen C S, Ooi B C. Collective spatial keyword querying. In: Proceedings of ACM SIGMOD International Conference on Management of Data. 2011, 373–384Google Scholar
  18. 18.
    Li G L, Feng J H, Xu J. Desks: direction-aware spatial keyword search. In: Proceedings of the 28th IEEE International Conference on Data Engineering. 2012, 474–485Google Scholar
  19. 19.
    Sheng C, Tao Y F. FIFO indexes for decomposable problems. In: Proceedings of the 30th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems. 2011, 25–35Google Scholar
  20. 20.
    Hjaltason G R, Samet H. Distance browsing in spatial databases. ACM Transactions on Database Systems, 1999, 24(2): 265–318CrossRefGoogle Scholar
  21. 21.
    Nemhauser G L, Wolsey L A, Fisher M L. An analysis of approximations for maximizing submodular set functions. Mathematical Programming, 1978, 14(1): 265–294MathSciNetCrossRefMATHGoogle Scholar
  22. 22.
    Feige U. A threshold of ln n for approximating set cover. Journal of the ACM, 1998, 45(4): 634–652MathSciNetCrossRefMATHGoogle Scholar
  23. 23.
    Sviridenko M. A note on maximizing a submodular set function subject to a knapsack constraint. Operations Research Letters, 2004, 32(1): 41–43MathSciNetCrossRefMATHGoogle Scholar
  24. 24.
    Berinde R, Cormode G, Indyk P, Strauss M J. Space-optimal heavy hitters with strong error bounds. In: Proceedings of ACM SIGMODSIGACT-SIGART Symposium on Principles of Database Systems. 2009, 157–166Google Scholar
  25. 25.
    Metwally A, Agrawal D, El Abbadi A. Efficient computation of frequent and top-k elements in data streams. In: Proceedings of International Conference on Database Theory. 2005, 398–412Google Scholar
  26. 26.
    Cudré-Mauroux P, Wu E, Madden S. Trajstore: an adaptive storage system for very large trajectory data sets. In: Proceedings of the 26th IEEE International Conference on Data Engineering. 2010, 109–120Google Scholar
  27. 27.
    Achakeev D, Seeger B, Widmayer P. Sort-based query-adaptive loading of R-trees. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management. 2012, 2080–2084Google Scholar
  28. 28.
    Sleator D D, Tarjan R E. Self-adjusting binary search trees. Journal of the ACM, 1985, 32(3): 652–686MathSciNetCrossRefMATHGoogle Scholar
  29. 29.
    Park E, Mount D M. A self-adjusting data structure for multidimensional point sets. In: Proceedings of European Symposium on Algorithms. 2012, 778–789Google Scholar
  30. 30.
    Idreos S, Kersten M L, Manegold S. Database cracking. In: Proceedings of Innovative Data Systems Research. 2007, 68–78Google Scholar
  31. 31.
    Tzoumas K, Yiu ML, Jensen C S. Workload-aware indexing of continuously moving objects. Proceedings of the VLDB Endowment, 2009, 2(1): 1186–1197CrossRefGoogle Scholar

Copyright information

© Higher Education Press and Springer-Verlag GmbH Germany 2018

Authors and Affiliations

  1. 1.Systems Engineering and Engineering ManagementThe Chinese University of Hong KongHong KongChina

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