A Method of Aggregate Query Matching in Semantic Cache for Massive Database Applications

  • Jianyu Cai
  • Yan Jia
  • Shuqiang Yang
  • Peng Zou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3756)


Aggregate queries are frequent in massive database applications. Their execution tends to be time consuming and costly. Therefore efficiently executing aggregate queries is very important. Semantic cache is a novel method for aiding query evaluation that reuses results of previously answered queries. But little work has been done on semantic cache involving aggregate queries. This is a limiting factor in its applicability. To use semantic cache in massive database applications, it is necessary to extend semantic cache to process aggregate query. In this paper, query matching is identified as a foundation for answering aggregate query by semantic caches. Firstly a formal semantic cache model for aggregate query is proposed. Based on this model, we discuss aggregate query matching. Two algorithms are presented for aggregate query matching. These two algorithms have been implemented in a massive database application project. The practice shows the algorithms are efficient.


Aggregation Function Large Data Base Aggregate Query Match Type Query Match 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Dar, S., Franklin, M.J., Jonsson, B.T., Srivastava, D., Tan, M.: Semantic data caching and replacement. In: Proc. of 22th Int’l Conf. on Very Large Data Bases, Mumbai (Bombay), pp. 330–341. Morgan Kaufmann, India (1996)Google Scholar
  2. 2.
    Godfrey, P., Gryz, J.: Answering Queries by Semantic Caches. In: Bench-Capon, T.J.M., Soda, G., Tjoa, A.M. (eds.) DEXA 1999. LNCS, vol. 1677, pp. 485–498. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  3. 3.
    Ren, Q., Dunham, M.H., Kumar, V.: Semantic Caching and Query Processing. IEEE Transactions on Knowledge and Data Engineering 15(1), 192–210 (2003)CrossRefGoogle Scholar
  4. 4.
    Basu, J.: Associative caching in client-server databases: [PhD dissertation]. Stanford University (1998)Google Scholar
  5. 5.
    Lee, D., Chu, W.W.: Semantic caching via query matching for web sources. In: Proc. of the 8th international conference on Information and knowledge management, pp. 77–85. ACM Press, Kansas City (1999)Google Scholar
  6. 6.
    Zaharioudakis, M., Cochrane, R., Lapis, G., Pirahesh, H., Urata, M.: Answering Complex SQL Queries Using Automatic Summary Tables. In: Proc. ACM SIGMOD Int’l Conf. on Management of Data, pp. 105–116. ACM Press, Dallas (2000)CrossRefGoogle Scholar
  7. 7.
    Gupta, A., Harinarayan, V., Quass, D.: Aggregate-query processing in data warehousing environments. In: Proc. of 21th Int’l Conf. on Very Large Data Bases, pp. 358–369. Morgan Kaufmann, Zurich (1995)Google Scholar
  8. 8.
    Srivastava, D., Dar, S., Jagadish, H.V., Levy, A.: Answering queries with aggregation using views. In: Proc. of 22th Int’l Conf. on Very Large Data Bases, Mumbai (Bombay), pp. 318–329. Morgan Kaufmann, India (1996)Google Scholar
  9. 9.
    Cohen, S., Nutt, W.: Rewriting aggregate queries using views. In: Proc. of 18th Symposium on Principles of Database Systems, May 1999, ACM Press, Philadelphia (1999)Google Scholar
  10. 10.
    Sun, X.H., Kamel, N., Ni, L.M.: Solving implication problems in database applications. In: Proc. ACM SIGMOD Int’l. Conf. on Management of Data, pp. 185–192. ACM Press, Portland (1989)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jianyu Cai
    • 1
  • Yan Jia
    • 1
  • Shuqiang Yang
    • 1
  • Peng Zou
    • 1
  1. 1.School of ComputerNational University of Defense TechnologyChangshaChina

Personalised recommendations