A Knowledge Based Approach for Query Optimization in Preferential Mapping Relational Databases
Keywords:
query optimization, relational databases, query plan, preferential databases, query evaluation, query parser, dynamic query optimization algorithmAbstract
Relational query databases provide a high level declarative interface to access data stored in relational databases. Two key components of the query evaluation component of a SQL database system are the query optimizer and the query execution engine. System R optimization framework since this was a remarkably elegant approach that helped fuel much of the subsequent work in optimization. Transparent and efficient evaluations of preferential queries are allowed by relational database systems. This results in experimenting extensive evaluation on two real world data sets which illustrates the feasibility and advantages of the framework. Early pruning of results based on score or confidence during query processing are enabled by combining the prefer operator with the rank and rank join operators. During preference evaluation, both the conditional and the scoring part of a preference are used. The conditional part acts as a soft constraint that determines which records are scored without disqualifying any duplicates from the query result. To introduce a preferences mapping relational data model that extends database with profile preferences for query optimizing and an extended algebra that captures the essence of processing queries with ranking method. Based on a set of algebraic properties and a cost model that to propose, to provide several query optimization strategies for extended query plans. To describe a query execution algorithm that blends preference evaluation with query execution, while making effective use of the native query engine.
References
Hong, W. Parallel Query Processing Using Shared Memory Multiprocessors and Disk Arrays. Ph.D. Thesis, University of California, Berkeley, 1992.
Bizarro, P., Bruno, N., De Witt, D.J.: Progressive Parametric Query Optimization. IEEE Transactions on Knowledge and Data Engineering 21(4), 582 – 594 (2009).
Kießling W., Hafenrichter B. (2002): Optimizing Preference Queries for Personalized Web Services. In Proceedings of the IASTED International Conference, Communications, Internet and Information Technology (CIIT 2002), St. Thomas, Virgin Islands, USA, 461 - 466.
Kießling W., Hafenrichter B. (2003): Algebraic Optimization of Relational Preference Queries, Technical Report 2003-1, University of Augsburg, Germany.
Kießling W., Köstler G. (2002): Preference SQL Design, Implementation, Experiences. Proceedings of 28th International Conference on Very Large Data Bases, Hong Kong, China, 990 -1001.
Köstler G., Kießling W., Thöne H.,Güntzer U.(1995): Fixpoint Iteration with Subsumption in Deductive Databases. Journal of Intelligent Information Systems, 4(2): 123 - 148.
Kossmann D., Ramsak F., Rost S. (2002): Shooting Stars in the Sky: An Online Algorithm for Skyline Queries. Proceedings of 28th International Conference on Very Large Data Bases, Hong Kong, China, 275 - 286.
Lacroix M., Lavency P. (1987): Preferences: Putting More Knowledge into Queries. Proceedings of 13th International Conference on Very Large Data Bases, Brighton, UK, 217 - 225.
Selinger P.G., Astrahan M.M., Chamberlin D.D., Lorie R.A., Price T.G. (1979): Access Path Selection in a Relational Database Management System. Proceedings of the 1979 ACM SIGMOD International Conference on Management of Data, Boston, USA, 23 - 34.
Tan K.-L., Eng P.-K., Ooi B. C. (2001): Efficient Progressive Skyline Computation. Proceedings of 27the International Conference on Very Large Data Bases, Rome, Italy, 301 - 310.
Minyar Sass [MA05]i, and Amel Grissa-Touzi “Contribution to the Query Optimization in the Object-Oriented Databases” World Academy of Science, Engineering and Technology 11 2005.
Nikose M.C. Dhande.S.S, Dr. G. R. Bamnote Query “Optimization in Object Oriented Databases through Detecting Independent Sub queries”.
Navta Kumari “Query Optimization Techniques-Tips for writing Efficient Queries”, International Journal of Scientific and Research Publications, Volume 2, Issue 6, June 2012.
Spaccapietra.S and Parent.C, “A step forward in solving structural conflicts,” IEEE Transactions on Knowledge 5and Data Engineering, vol. 6, no. 2, 1998.
Selinger, P.G., Astrahan, M.M., Chamberlin, D.D., Lorie, R.A.,Price T.G. Access Path Selection in a Relational Database System. In Readings in Database Systems. Morgan Kaufman.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2014 COMPUSOFT: An International Journal of Advanced Computer Technology
![Creative Commons License](http://i.creativecommons.org/l/by/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution 4.0 International License.
©2023. COMPUSOFT: AN INTERNATIONAL OF ADVANCED COMPUTER TECHNOLOGY by COMPUSOFT PUBLICATION is licensed under a Creative Commons Attribution 4.0 International License. Based on a work at COMPUSOFT: AN INTERNATIONAL OF ADVANCED COMPUTER TECHNOLOGY. Permissions beyond the scope of this license may be available at Creative Commons Attribution 4.0 International Public License.