December  2015, 8(6): 1341-1356. doi: 10.3934/dcdss.2015.8.1341

Statistical query-based rule derivation system by backward elimination algorithm

1. 

Dept. of Mathematical Engineering, Yildiz Technical University, Istanbul, 34210, Turkey, Turkey

Received  July 2015 Revised  September 2015 Published  December 2015

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.
Citation: Ayla Sayli, Ayse Oncu Sarihan. Statistical query-based rule derivation system by backward elimination algorithm. Discrete & Continuous Dynamical Systems - S, 2015, 8 (6) : 1341-1356. doi: 10.3934/dcdss.2015.8.1341
References:
[1]

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

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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.  doi: 10.1145/185827.185828.  Google Scholar

[3]

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

[4]

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

[5]

F. De Marchi, S. Lopes and J.-M. Petit, Efficient algorithms for mining inclusion dependencies,, in Advances in Database Technology-EDBT 2002, (2002), 464.  doi: 10.1007/3-540-45876-X_30.  Google Scholar

[6]

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

[7]

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

[8]

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.  doi: 10.1145/1282480.1282483.  Google Scholar

[9]

C.-N. Hsu and C. A. Knoblock, Learning Database Abstractions for Query Reformulation,, University of Southern California, (1993).   Google Scholar

[10]

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, (2002).   Google Scholar

[11]

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, (1993).   Google Scholar

[12]

N. S. Ishakbeyoǧlu and Z. M. Özsoyoǧlu, On the maintenance of implication integrity constraints,, in Database and Expert Systems Applications, (1993), 221.   Google Scholar

[13]

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, (1981), 510.   Google Scholar

[14]

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.  doi: 10.1016/S0950-5849(00)00121-X.  Google Scholar

[15]

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

[16]

V. Pudi and J. R. Haritsa, Generalized closed itemsets for association rule mining,, in Data Engineering, (2003), 714.   Google Scholar

[17]

A. Sayli and A. Elibol, A rule rectification algorithm on semantic query optimisation,, in 17th International Conference on Systems Research, 6 (2005), 12.   Google Scholar

[18]

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

[19]

A. Sayli and B. Gokce, The use of SQO rules in DMLs,, in Computers and Communications, (2004), 146.  doi: 10.1109/ISCC.2004.1358396.  Google Scholar

[20]

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

[21]

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

[22]

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

[23]

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.  doi: 10.1109/69.250077.  Google Scholar

[24]

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.  doi: 10.1145/146931.146932.  Google Scholar

[25]

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.  doi: 10.1109/69.87981.  Google Scholar

show all references

References:
[1]

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

[2]

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.  doi: 10.1145/185827.185828.  Google Scholar

[3]

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

[4]

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

[5]

F. De Marchi, S. Lopes and J.-M. Petit, Efficient algorithms for mining inclusion dependencies,, in Advances in Database Technology-EDBT 2002, (2002), 464.  doi: 10.1007/3-540-45876-X_30.  Google Scholar

[6]

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

[7]

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

[8]

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.  doi: 10.1145/1282480.1282483.  Google Scholar

[9]

C.-N. Hsu and C. A. Knoblock, Learning Database Abstractions for Query Reformulation,, University of Southern California, (1993).   Google Scholar

[10]

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, (2002).   Google Scholar

[11]

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, (1993).   Google Scholar

[12]

N. S. Ishakbeyoǧlu and Z. M. Özsoyoǧlu, On the maintenance of implication integrity constraints,, in Database and Expert Systems Applications, (1993), 221.   Google Scholar

[13]

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, (1981), 510.   Google Scholar

[14]

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.  doi: 10.1016/S0950-5849(00)00121-X.  Google Scholar

[15]

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

[16]

V. Pudi and J. R. Haritsa, Generalized closed itemsets for association rule mining,, in Data Engineering, (2003), 714.   Google Scholar

[17]

A. Sayli and A. Elibol, A rule rectification algorithm on semantic query optimisation,, in 17th International Conference on Systems Research, 6 (2005), 12.   Google Scholar

[18]

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

[19]

A. Sayli and B. Gokce, The use of SQO rules in DMLs,, in Computers and Communications, (2004), 146.  doi: 10.1109/ISCC.2004.1358396.  Google Scholar

[20]

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

[21]

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

[22]

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

[23]

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.  doi: 10.1109/69.250077.  Google Scholar

[24]

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.  doi: 10.1145/146931.146932.  Google Scholar

[25]

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.  doi: 10.1109/69.87981.  Google Scholar

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