Effective Cost Models for Predicting Web Query Execution Cost
Shashidhara H R1, Sanjay P K2, G T Raju3, Vinayaka Murthy4
1Dr. Shashidhara H R, Associate Professor, Department of Computer Science & Engineering, RNS Institute of Technology, Bengaluru (Karnataka), India.
2Sanjay P K, Assistant Professor, Department of Computer Science & Engineering, RNS Institute of Technology, Bengaluru (Karnataka), India.
3G T Raju, Department of Computer Science & Engineering, RNS Institute of Technology, Bengaluru (Karnataka), India.
4Vinayaka Murthy, Department of Computer Science & Engineering, RNS Institute of Technology, Bengaluru (Karnataka), India.
Manuscript received on 04 December 2019 | Revised Manuscript received on 12 December 2019 | Manuscript Published on 31 December 2019 | PP: 213-218 | Volume-9 Issue-2S December 2019 | Retrieval Number: B11271292S19/2019©BEIESP | DOI: 10.35940/ijitee.B1127.1292S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Classical query optimizers rely on sophisticated cost models to estimate the cost of executing a query and its operators. By using this cost model, an efficient global plan is created by the optimizer which will be used to execute a given query. This cost modeling facility is difficult to be implemented in Web query engines because many local data sources might not be comfortable in sharing meta data information due to confidentiality issues. In this work, an efficient and effective cost modeling techniques for Web query engines are proposed. These techniques does not force the local data sources to reveal their meta data but employs a learning mechanism to estimate the cost of executing a given local query. Two cost modeling algorithms namely: Poisson cost model and Exponential cost model algorithms are presented. Empirical results over real world datasets reveal the efficiency and effectiveness of the new cost models.
Keywords: Cost Models, Web Query Optimization, Mediator, Operators.
Scope of the Article: Web Algorithms