Predicting The Efficiency of Difficult Queries over Databases using SRC
Shyamily Kuriakose1, Rosna P. Haroon2
1Kumari Shyamily Kuriakose, Department of Computer Science and Engineering, Ilahia College of Engineering and Technology, Mulavoor, Muvattupuzha (Kerala), India.
2Prof. Rosna P Haroon, Department of Computer Science and Engineering, Ilahia College of Engineering and Technology, Mulavoor, Muvattupuzha (Kerala), India.
Manuscript received on 13 October 2015 | Revised Manuscript received on 22 October 2015 | Manuscript Published on 30 October 2015 | PP: 36-38 | Volume-5 Issue-5, October 2015 | Retrieval Number: E2211105515/15©BEIESP
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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: Keyword queries on databases provide easy access to data, but often suffer from low ranking quality, i.e., low precision and/or recall, as shown in recent benchmarks. It would be useful to identify queries that are likely to have low ranking quality to improve the user satisfaction. For instance, the system may suggest to the user alternative queries for such hard queries. In this paper, we analyze the characteristics of hard queries and propose a novel framework to measure the degree of difficulty for a keyword query over a database, considering both the structure and the content of the database and the query results. We evaluate our query difficulty prediction model against two effectiveness benchmarks for popular keyword search ranking methods. Our empirical results show that our model predicts the hard queries with high accuracy. The proposed method use two level corruption module compare to structured robustness algorithm
Keywords: Keyword Query, Query Effectiveness, Robustness.
Scope of the Article: Data Mining