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

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Web-Age Information Management (WAIM 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7901))

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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.

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Author information

Authors and Affiliations

  1. University of Chinese Academy of Sciences (UCAS), 19(A) Yuquan Rd., Shi**gshan District, Bei**g, 100049, China

    Zhu Wang & Tiejian Luo

  2. University of Technology, Sydney, P.O. Box 123, Broadway, NSW, 2007, Australia

    Guandong Xu & **ang Wang

Authors
  1. Zhu Wang
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  2. Tiejian Luo
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  3. Guandong Xu
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  4. **ang Wang
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Editor information

Editors and Affiliations

  1. College of Computer Science, Zhejiang University, Hangzhou, China

    Yunjun Gao

  2. Seoul National University, Seoul, Korea

    Kyuseok Shim

  3. Institute of Software, Chinese Academy of Sciences, South-Fourth-Street 4, Zhong-Guan-Cun, 100190, Bei**g, P.R. China

    Zhiming Ding

  4. School of Computer Science and Technology, University of Science and Technology of China, 230027, Hefei, China

    Peiquan **

  5. School of Computer Science and Technology, Hangzhou Dianzi University, 310018, Hangzhou, China

    Zujie Ren

  6. Key Laboratory of Intelligence Computing and Novel Software Technology, Tian** Key Laboratory of Computer Vision and System, Ministry of Education, Tian** University of Technology, 300384, Tian**, China

    Yingyuan **ao

  7. CityU-USTC Advanced Research Institute, Suzhou, China

    An Liu

  8. School of Information Science and Technology, Southwest Jiaotong University, 610031, Chengdu, China

    Shaojie Qiao

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Wang, Z., Luo, T., Xu, G., Wang, X. (2013). A New Indexing Technique for Supporting By-attribute Membership Query of Multidimensional Data. In: Gao, Y., et al. Web-Age Information Management. WAIM 2013. Lecture Notes in Computer Science, vol 7901. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39527-7_27

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  • DOI: https://doi.org/10.1007/978-3-642-39527-7_27

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39526-0

  • Online ISBN: 978-3-642-39527-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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