Advanced Search
Article Contents
Article Contents

Statistical query-based rule derivation system by backward elimination algorithm

Abstract Related Papers Cited by
  • Computers play a serious role in human life, especially web-based applications running twenty four hours per day. These applications are based on relational database management system and they receive many queries from the users. These queries are executed in the commercial systems one by one without any consideration of past experiences and data analysis. The execution of queries can be faster if some rules were derived from the past queries. In this paper, we propose a statistical query-based rule derivation system by the backward elimination algorithm, which analysis the data based on the past queries in order to derive new rules, and then it uses these rules for the execution of new queries. The computational results are presented and analysed that the system is very efficient and promising.
    Mathematics Subject Classification: Primary: 68P15; Secondary: 68Q55.


    \begin{equation} \\ \end{equation}
  • [1]

    J. Bakus and M. S. Kamel, Higher order feature selection for text classification, Knowledge and Information Systems, 9 (2006), 468-491.doi: 10.1007/s10115-005-0209-6.


    S. Ceri, P. Fraternali, S. Paraboschi and L. Tanca, Automatic generation of production rules for integrity maintenance, ACM Transactions on Database Systems, 19 (1994), 367-422.doi: 10.1145/185827.185828.


    U. S. Chakravarthy, J. Grant and J. Minker, Logic-based approach to semantic query optimization, ACM Transactions on Database Systems), 15 (1990), 162-207.doi: 10.1145/78922.78924.


    K. C. Chan and A. K. Wong, A statistical technique for extracting classificatory knowledge from databases, Discovery in Databases, (1991), 107-124.


    F. De Marchi, S. Lopes and J.-M. Petit, Efficient algorithms for mining inclusion dependencies, in Advances in Database Technology-EDBT 2002, {2287}, Springer, 2002, 464-476.doi: 10.1007/3-540-45876-X_30.


    D. Genest and M. Chein, A content-search information retrieval process based on conceptual graphs, Knowledge and Information Systems, 8 (2005), 292-309.doi: 10.1007/s10115-004-0179-0.


    G. Graefe and D. J. DeWitt, The EXODUS Optimizer Generator, vol. 16, ACM, 1987.


    M. M. Hammer and D. J. McLeod, Semantic integrity in a relational data base system, in Proceedings of the 1st International Conference on Very Large Data Bases, 1975, 25-47.doi: 10.1145/1282480.1282483.


    C.-N. Hsu and C. A. Knoblock, Learning Database Abstractions for Query Reformulation, University of Southern California, Information Sciences Institute, 1993.


    I. K. Ibrahim, V. Dignum, W. Winiwarter and E. Weippl, Logic based approach to semantic query transformation for knowledge management applications, Proceeding of the International Conference on Knowledge Management, Berlin, Springer, 2002.


    I. Imam, R. Michalski and L. Kerschberg, Discovering attribute dependence in databases by integrating symbolic learning and statistical analysis techniques, in Proceeding of the AAAI-93 Workshop on Knowledge Discovery in Databases, Washington DC, 1993.


    N. S. Ishakbeyoǧlu and Z. M. Özsoyoǧlu, On the maintenance of implication integrity constraints, in Database and Expert Systems Applications, Springer, 1993, 221-232.


    J. J. King, Quist: A system for semantic query optimization in relational databases, in Proceedings of the seventh international conference on Very Large Data Bases-Volume 7, VLDB Endowment, 1981, 510-517.


    S.-G. Lee, L. J. Henschen, J. Chun and T. Lee, Identifying relevant constraints for semantic query optimization, Information and Software Technology, 42 (2000), 899-914.doi: 10.1016/S0950-5849(00)00121-X.


    G. Piatetsky-Shapiro and C. Matheus, Measuring data dependencies in large databases, in Knowledge Discovery in Databases Workshop, 1993, 162-173.


    V. Pudi and J. R. Haritsa, Generalized closed itemsets for association rule mining, in Data Engineering, 2003. Proceedings. 19th International Conference on, IEEE, 2003, 714-716.


    A. Sayli and A. Elibol, A rule rectification algorithm on semantic query optimisation, in 17th International Conference on Systems Research, Informatics and Cybernetics Focus on Advances in Decision Technology and Intelligent Information Systems, 6 (2005), 12-16.


    A. Sayli and A. Elibol, Rule evaluation algorithm for semantic query optimisation, Applied Mathematics and Information Sciences, 7 (2013), 1773-1781.doi: 10.12785/amis/070515.


    A. Sayli and B. Gokce, The use of SQO rules in DMLs, in Computers and Communications, 2004. Proceedings. ISCC 2004. Ninth International Symposium on, vol. 1, IEEE, 2004, 146-151.doi: 10.1109/ISCC.2004.1358396.


    A. Sayli and B. Lowden, A fast transformation method to semantic query optimisation, in International Database Engineering and Applications Symposium, IEEE, 1997, 319-326.doi: 10.1109/IDEAS.1997.625701.


    A. Sayli and B. Lowden, Ensuring rule consistency in the presence of db updates, in Proc. XII International Symposium on Computer and Information Sciences, Turkey, 1997.


    A. Sayli and O. Uysal, A dynamic self-learning method for semantic query optimisation, International Journal of Technology, Policy and Management, 8 (2008), 126-147.doi: 10.1504/IJTPM.2008.017216.


    S. Shekhar, B. Hamidzadeh, A. Kohli and M. Coyle, Learning transformation rules for semantic query optimization: A data-driven approach, IEEE Transactions on Knowledge & Data Engineering, 5 (2002), 950-964.doi: 10.1109/69.250077.


    M. Siegel, E. Sciore and S. Salveter, A method for automatic rule derivation to support semantic query optimization, ACM Transactions on Database Systems, 17 (1992), 563-600.doi: 10.1145/146931.146932.


    C. T. Yu and W. Sun, Automatic knowledge acquisition and maintenance for semantic query optimization, IEEE Transactions on Knowledge and Data Engineering, 1 (1989), 362-375.doi: 10.1109/69.87981.

  • 加载中

Article Metrics

HTML views() PDF downloads(172) Cited by(0)

Access History

Other Articles By Authors



    DownLoad:  Full-Size Img  PowerPoint