Backgrounds of Searchable Storage

  • Yu HuaEmail author
  • Xue Liu


Multi-dimensional data indexing has received much research attention recently in a centralized system. However, it remains a nascent area of research in providing an integrated structure for multiple queries on multi-dimensional data in a distributed environment. We propose a new data structure, called BR-tree (Bloom filter based R-tree), and implement such a prototype in the context of a distributed system. The node in a BR-tree, viewed as an expansion from the traditional R-tree node structure, incorporates space-efficient Bloom filters to facilitate fast membership queries. The proposed BR-tree can simultaneously support not only existing point and range queries but also cover and bound queries that can potentially benefit various data indexing services. Compared with previous data structures, BR-tree achieves space efficiency and provides quick response (\(\le O(log~n)\)) on these four types of queries. Our extensive experiments in a distributed environment further validate the practicality and efficiency of the proposed BR-tree structure (©{2009}IEEE. Reprinted, with permission, from Ref. [1].).


  1. 1.
    Y. Hua, B. Xiao, J. Wang, BR-tree: a scalable prototype for supporting multiple queries of multidimensional data. IEEE Trans. Comput. (TC) 58, 1585–1598 (2009)MathSciNetCrossRefGoogle Scholar
  2. 2.
    R. Devine, Design and implementation of DDH: a distributed dynamic hashing algorithm, in Proceedings of the 4th International Conference on Foundations of Data Organizations and Algorithms (1993), pp. 101–114Google Scholar
  3. 3.
    Distributed hash tables links,
  4. 4.
    M. Harren, J.M. Hellerstein, R. Huebsch, B.T. Loo, S. Shenker, I. Stoica, Complex queries in DHT-based peer-to-peer networks, in Proceedings of the IPTPS (2002)Google Scholar
  5. 5.
    Y. Hua, Y. Zhu, H. Jiang, D. Feng, L. Tian, Scalable and adaptive metadata management in ultra large-scale file systems, in Proceedings of the ICDCS (2008), pp. 403–410Google Scholar
  6. 6.
    Y. Hua, D. Feng, H. Jiang, L. Tian, RBF: a new storage structure for space-efficient queries for multidimensional metadata in OSS, in FAST Work-in-Progress Reports (2007)Google Scholar
  7. 7.
    L. Arge, M. de Berg, H.J.Haverkort, K. Yi, The priority R-tree: a practically efficient and worst-case optimal R-tree, in Proceedings of the ACM SIGMOD, pp. 347–358 (2004)Google Scholar
  8. 8.
    A. Guttman, R-trees: a dynamic index structure for spatial searching, in Proceedings of the ACM SIGMOD (1984), pp. 47–57CrossRefGoogle Scholar
  9. 9.
    C. du Mouza, W. Litwin, P. Rigaux, SD-Rtree: a scalable distributed Rtree, in Proceedings of the ICDE (2007), pp. 296–305Google Scholar
  10. 10.
    V. Gaede, O. Günther, Multidimensional access methods. ACM Comput. Surv. 30(2), 170–231 (1998)CrossRefGoogle Scholar
  11. 11.
    E. Bertino, B.C. Ooi, R. Sacks-Davis, K.-L. Tan, J. Zobel, B. Shidlovsky, B. Cantania, Indexing Techniques for Advanced Database Applications (Kluwer Academics, 1997)Google Scholar
  12. 12.
    B. Bloom, Space/time trade-offs in hash coding with allowable errors. Commun. ACM 13(7), 422–426 (1970)CrossRefGoogle Scholar
  13. 13.
    A. Broder, M. Mitzenmacher, Network applications of Bloom filters: a survey. Internet Math. 1, 485–509 (2005)MathSciNetCrossRefGoogle Scholar
  14. 14.
    A. Broder, M. Mitzenmacher, Using multiple hash functions to improve IP lookups, in Proceedings of the INFOCOM (2001), pp. 1454–1463Google Scholar
  15. 15.
    F. Baboescu, G. Varghese, Scalable packet classification. IEEE/ACM Trans. Netw. 13(1), 2–14 (2005)CrossRefGoogle Scholar
  16. 16.
    S. Dharmapurikar, P. Krishnamurthy, D.E. Taylor, Longest prefix matching using Bloom filters, in Proceedings of the ACM SIGCOMM (2003), pp. 201–212Google Scholar
  17. 17.
    Y. Hua, B. Xiao, A multi-attribute data structure with parallel Bloom filters for network services, Proceedings of the IEEE International Conference on High Performance Computing (HiPC) (2006), pp. 277–288Google Scholar
  18. 18.
    B. Xiao, Y. Hua, Using parallel Bloom filters for multi-attribute representation on network services. IEEE Trans. Parallel Distrib. Syst. (2009)Google Scholar
  19. 19.
    L. Fan, P. Cao, J. Almeida, A. Broder, Summary cache: a scalable wide-area web cache sharing protocol. IEEE/ACM Trans. Netw. 8(3), 281–293 (2000)CrossRefGoogle Scholar
  20. 20.
    M. Mitzenmacher, Compressed Bloom filters. IEEE/ACM Trans. Netw. 10(5), 604–612 (2002)CrossRefGoogle Scholar
  21. 21.
    A. Kumar, J.J. Xu, J. Wang, O. Spatschek, L.E. Li, Space-code Bloom filter for efficient per-flow traffic measurement, in Proceedings of the INFOCOM (2004), pp. 1762–1773Google Scholar
  22. 22.
    C. Saar, M. Yossi, Spectral Bloom filters, in Proceedings of the ACM SIGMOD (2003), pp. 241–252Google Scholar
  23. 23.
    D. Guo, J. Wu, H. Chen, X. Luo, Theory and network application of dynamic Bloom filters, in Proceedings of the INFOCOM (2006)Google Scholar
  24. 24.
    F. Hao, M. Kodialam, T.V. Lakshman, Incremental Bloom filters, in Proceedings of the INFOCOM (2008), pp. 1741–1749Google Scholar
  25. 25.
    T.K. Sellis, N. Roussopoulos, C. Faloutsos, The \(R^+\)-tree: a dynamic index for multi-dimensional objects, in Proceedings of the VLDB (1987), pp. 507–518Google Scholar
  26. 26.
    N. Beckmann, H.-P. Kriegel, R. Schneider, B. Seeger, The R*-tree: an efficient and robust access method for points and rectangles, in Proceedings of the SIGMOD (1990), pp. 322–331CrossRefGoogle Scholar
  27. 27.
    C. Bohm, S. Berchtold, D.A. Keim, Searching in high-dimensional spaces index structures for improving the performance of multimedia databases. ACM Comput. Surv. 33(3), 322–373 (2001)CrossRefGoogle Scholar
  28. 28.
    J. Aspnes, G. Shah, Skip graphs, in Proceedings of the ACM-SIAM Symposium on Discrete Algorithms (SODA) (2003), pp. 384–393Google Scholar
  29. 29.
    A.R. Bharambe, M. Agrawal, S. Seshan, Mercury: supporting scalable multi-attribute range queries, Proceedings of the ACM SIGCOMM (2004), pp. 353–366CrossRefGoogle Scholar
  30. 30.
    C. Zheng, G. Shen, S. Li, S. Shenker, Distributed segment tree: support of range query and cover query over DHT, in Proceedings of the IPTPS (2006)Google Scholar
  31. 31.
  32. 32.
    J. Gao, P. Steenkiste, An adaptive protocol for efficient support of range queries in DHT-based systems, in Proceedings of the ICNP (2004), pp. 239–250Google Scholar
  33. 33.
    D. Li, J. Cao, X. Lu, K.C.C. Chan, B. Wang, J. Su, H. va Leong, A.T.S. Chan, Delay-bounded range queries in DHT-based peer-to-peer systems, in Proceedings of the ICDCS (2006)Google Scholar
  34. 34.
    X. Li, Y.J. Kim, R. Govindan, W. Hong, Multi-dimensional range queries in sensor networks, in Proceedings of the ACM SenSys (2003), pp. 63–75Google Scholar
  35. 35.
    H.V. Jagadish, B.C. Ooi, Q.H. Vu, R. Zhang, A. Zhou, VBI-tree: a peer-to-peer framework for supporting multi-dimensional indexing schemes, in Proceedings of the ICDE (2006)Google Scholar
  36. 36.
    H. Jagadish, B. Ooi, Q. Vu, BATON: a balanced tree structure for peer-to-peer networks, in Proceedings of the VLDB (2005), pp. 661–672Google Scholar
  37. 37.
    E. Riedel, M. Kallahalla, R. Swaminathan, A framework for evaluating storage system security, in Proceedings of the FAST (2002), pp. 15–30Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Huazhong University of Science and TechnologyWuhanChina
  2. 2.McGill UniversityMontrealCanada

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