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Keyword Oriented Bitmap Join Index for In-Memory Analytical Processing

  • Yansong Zhang
  • Mingchuan Su
  • Xuan Zhou
  • Shan Wang
  • Xue Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7923)

Abstract

Nowadays computers are equipped with multicore processors and large RAM to support high performance processing. In-memory analytical processing and just-in-time data warehousing have become realistic for various enterprises. An analytical query normally requires a small proportion of ‘hot’ data, usually defined by a set of keywords, instead of the entire data set, which involves large volume table scan and costly star joins. Therefore, identifying frequent keywords to retrieve hot data can dramatically reduce the cost of full table scan or star-join. In this paper, we propose a keyword oriented bitmap join index to improve the space efficiency and performance of in-memory data warehouse. Keyword oriented bitmap join index is a global bitmap join index for the entire data warehouse, as opposed to conventional bitmap join indexes which are indicated for specified attributes. With our index, star-join is first converted into keyword search and bitmap combination. The resulting bitmap filters are then employed to filter records. Through the filtering by bitmaps, a star-join is converted into positional scan on the fact table and additional dimension filtering. Both memory bandwidth and analytical performance can then be improved.

Keywords

keyword bitmap join index star join hot data 

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References

  1. 1.
  2. 2.
  3. 3.
    Boncz, P.A., Mangegold, S., Kersten, M.L.: Database architecture optimized for the new bottleneck: Memory access. In: VLDB, pp. 266–277 (1999)Google Scholar
  4. 4.
    Funke, F., Kemper, A., Neumann, T.: HyPer-sonic Combined Transaction AND Query Processing. PVLDB 4(12), 1367–1370 (2011)Google Scholar
  5. 5.
    O’Neil, P., O’Neil, B., Chen, X.: The Star Schema Benchmark (SSB), http://www.cs.umb.edu/~poneil/StarSchemaB.PDF
  6. 6.
  7. 7.
  8. 8.
    Levandoski, J., Larson, P., Stoica, R.: Identifying Hot and Cold Data in Main-Memory Databases. In: ICDE 2013 (2013)Google Scholar
  9. 9.
    Park, D., Du, D.H.C.: Hot data identification for flash-based storage systems using multiple bloom filters. In: Proceedings of the 2011 IEEE 27th Symposium on Mass Storage Systems and Technologies, MSST 2011, pp. 1–11 (2011)Google Scholar
  10. 10.
    Aouiche, K., Darmont, J., Boussaid, O., Bentayeb, F.: Automatic Selection of Bitmap Join Indexes in Data Warehouses. CoRR abs/cs/0703113 (2007)Google Scholar
  11. 11.
  12. 12.
  13. 13.
  14. 14.
    Zhang, Y., Wang, S., Lu, J.: Improving performance by creating a native join-index for OLAP. Frontiers of Computer Science in China 5(2), 236–249 (2011)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Abadi, D.J., Madden, S., Hachem, N.: Column-stores vs. row-stores: how different are they really? In: SIGMOD Conference 2008, pp. 967–980 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Yansong Zhang
    • 1
    • 3
  • Mingchuan Su
    • 1
    • 2
  • Xuan Zhou
    • 1
    • 2
  • Shan Wang
    • 1
    • 2
  • Xue Wang
    • 1
    • 2
  1. 1.School of InformationRenmin University of ChinaBeijingChina
  2. 2.DEKE LabRenmin University of ChinaBeijingChina
  3. 3.National Survey Research CenterRenmin University of ChinaBeijingChina

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