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A New Indexing Technique for Supporting By-attribute Membership Query of Multidimensional Data

  • Zhu Wang
  • Tiejian Luo
  • Guandong Xu
  • Xiang Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7901)

Abstract

Multidimensional Data indexing and lookup has been widely used in online data-intensive applications involving in data with multiple attributes. However, there remains a long way to go for the high performance multi-attribute data representation and lookup: the performance of index drops down with the increase of dimensions. In this paper, we present a novel data structure called Bloom Filter Matrix (BFM) to support multidimensional data indexing and by-attribute search. The proposed matrix is based on the Cartesian product of different bloom filters, each representing one attribute of the original data. The structure and parameter of each bloom filter is designed to fit the actual data characteristic and system demand, enabling fast object indexing and lookup, especially by-attribute search of multidimensional data. Experiments show that Bloom Filter Matrix is a fast and accurate data structure for multi-attribute data indexing and by-attribute search with high-correlated queries.

Keywords

Bloom Filter Matrix Multidimensional data indexing By-attribute search 

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References

  1. 1.
    Li, X., Kim, Y.J., Govindan, R.: Multi-dimensional Range Queries in Sensor Networks. In: SenSys 2003, pp. 63–75. ACM, New York (2003)Google Scholar
  2. 2.
    Liu, B., Lee, W.-C., Lee, D.L.: Distributed Caching of Multi-dimensional Data in Mobile Environments. In: MDM 2005, pp. 229–233. ACM, New York (2005)Google Scholar
  3. 3.
    Lin, C.X., Ding, B., Han, J., Zhu, F., Zhao, B.: Text Cube: Computing IR Measures for Multidimensional Text Database Analysis. In: ICDM 2008, pp. 905–910. ACM, New York (2008)Google Scholar
  4. 4.
    Jiang, N., Gruenwald, L.: Research Issues in Data Stream Association Rule Mining. ACM SIGMOD Record 35, 14–19 (2006)CrossRefGoogle Scholar
  5. 5.
    Nebot, V., Berlanga, R., Pérez, J.M., Aramburu, M.J., Pedersen, T.B.: Multidimensional Integrated Ontologies: A Framework for Designing Semantic Data Warehouses. Journal on Data Semantics 13, 1–36 (2009)CrossRefGoogle Scholar
  6. 6.
    Wang, Z., Luo, T.: Intelligent Video Content Routing in a Direct Access Network. In: SWS 2011, pp. 147–152. IEEE Press (2011)Google Scholar
  7. 7.
    Berners-Lee, T., Connolly, D., Kagal, L., Scharf, Y., Hendler, J.: N3Logic: A Logical Framework for the World Wide Web. Theory and Practice of Logic Programming 8, 249–269 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    HüNer, K.M., Otto, B., ÖSterle, H.: Collaborative Management of Business Metadata. International Journal of Information Management: The Journal for Information Professionals 31, 366–373 (2011)CrossRefGoogle Scholar
  9. 9.
    Bloom, B.: Space/time Trade-offs in Hash Coding with Allowable Errors. Communications of the ACM (CACM) 13, 422–426 (1970)CrossRefzbMATHGoogle Scholar
  10. 10.
    Mullin, J.: A Second Look at Bloom Filters. Communications of the ACM 26, 570–571 (1983)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Guo, D., Chen, H., Luo, X.: Theory and Network Applications of Dynamic Bloom Filters. In: INFOCOM 2006, pp. 1–12. IEEE Press (2006)Google Scholar
  12. 12.
    Nasre, R., Rajan, K., Govindarajan, R., Khedker, U.P.: Scalable Context-Sensitive Points-to Analysis Using Multi-dimensional Bloom Filters. In: Hu, Z. (ed.) APLAS 2009. LNCS, vol. 5904, pp. 47–62. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  13. 13.
    Belazzougui, D., Boldi, P., Pagh, R., Vigna, S.: Theory and Practice of Monotone Minimal Perfect Hashing. Journal of Experimental Algorithmics, Article No. 3.2 (2011)Google Scholar
  14. 14.
    Bruck, J., Gao, J., Jiang, A.: Weighted Bloom Filter. In: ISIT 2006, pp. 2304–2308. IEEE Press (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Zhu Wang
    • 1
  • Tiejian Luo
    • 1
  • Guandong Xu
    • 2
  • Xiang Wang
    • 2
  1. 1.University of Chinese Academy of Sciences (UCAS)BeijingChina
  2. 2.University of Technology, SydneyBroadwayAustralia

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