Advertisement

A New Scale for Attribute Dependency in Large Database Systems

  • Soumya Sen
  • Anjan Dutta
  • Agostino Cortesi
  • Nabendu Chaki
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7564)

Abstract

Large, data centric applications are characterized by its different attributes. In modern day, a huge majority of the large data centric applications are based on relational model. The databases are collection of tables and every table consists of numbers of attributes. The data is accessed typically through SQL queries. The queries that are being executed could be analyzed for different types of optimizations. Analysis based on different attributes used in a set of query would guide the database administrators to enhance the speed of query execution. A better model in this context would help in predicting the nature of upcoming query set. An effective prediction model would guide in different applications of database, data warehouse, data mining etc. In this paper, a numeric scale has been proposed to enumerate the strength of associations between independent data attributes. The proposed scale is built based on some probabilistic analysis of the usage of the attributes in different queries. Thus this methodology aims to predict future usage of attributes based on the current usage.

Keywords

Materialized view Query Processing Attribute dependency Numeric scale Query Optimization 

References

  1. 1.
    Mukkamala, R.: Improving database performance through query standardization. In: IEEE Proceedings of Energy and Information Technologies in the Southeast, Southeastcon 1989 (1989)Google Scholar
  2. 2.
    Chiang, L., Chi Sheng, S., Chen Huei, Y.: Optimizing large join queries using a graph-based approach. IEEE Transactions on Knowledge and Data Engineering (March/April 2001)Google Scholar
  3. 3.
    Chin Wang, J., Tzong Horng, J., Ming Hsu, Y., Jhinue Liu, B.: A genetic algorithm for set query optimization in distributed database systems. In: IEEE International Conferences on Systems, Man, and Cybernetic (1996)Google Scholar
  4. 4.
    Bruno, N., Galindo-Legaria, C., Joshi, M.: Polynomial heuristics for query optimization. In: 26th IEEE International Conferences on Data Engineering (ICDE 2010) (2010)Google Scholar
  5. 5.
    Sarathy, V.M., Saxton, L.V., Van Gucht, D.: Algebraic foundation and optimization for object based query languages. In: Proceedings of 9th International Conference on Data Engineering (1993)Google Scholar
  6. 6.
    Ordonez, C.: Optimization of Linear Recursive Queries in SQL. IEEE Transactions on Knowledge and Data Engineering (2010)Google Scholar
  7. 7.
    Zhuang, Y., Qing, L., Chen, L.: Multi-query Optimization for Distributed Similarity Query Processing. In: 28th International Conference on Distributed Computing Systems, ICDCS 2008 (2008)Google Scholar
  8. 8.
    Yang, J., Karlapalem, K., Li, Q.: A framework for designing materialized views in data warehousing environment. In: Proceedings of 17th IEEE International Conference on Distributed Computing Systems, Maryland, U.S.A. (May 1997)Google Scholar
  9. 9.
    Gupta, H.: Selection of Views to Materialize in a DataWarehouse. In: Afrati, F.N., Kolaitis, P.G. (eds.) ICDT 1997. LNCS, vol. 1186, pp. 98–112. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  10. 10.
    Ashadevi, B., Subramanian, R.: Optimized Cost Effective Approach for Selection of Materialized views in Data Warehousing. International Journal of Computer Science and Technology 9(1) (April 2009)Google Scholar
  11. 11.
    Bhagat, P.A., Harle, R.B.: Materialized view management in peer to peer environment. In: International Conference and Workshop on Emerging Trends in Technology, ICWET 2011 (2011)Google Scholar
  12. 12.
    Goyal, N., Zaveri, K.S., Sharma, Y.: Improved Bitmap Indexing Strategy for Data Warehouses. In: Proceedings of 9th International Conference on Information Technology, ICIT 2006 (2006)Google Scholar
  13. 13.
    Aouiche, K., Darmont, J.: Data Mining Based Materialized View and Index Selection in Data Warehouses. Proceedings of J. Intell. Inf. Syst. (2009)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Soumya Sen
    • 1
  • Anjan Dutta
    • 1
  • Agostino Cortesi
    • 1
  • Nabendu Chaki
    • 2
  1. 1.University of CalcuttaKolkataIndia
  2. 2.Universita Ca FoscariVeniceItaly

Personalised recommendations