Privacy Aware Monitoring Framework for Moving Top-K Spatial Join Queries
U.L.Sindhu1, V.Sindhu2, P.S.Balamurugan3
1U.L.Sindhu, pursuing M.E. Computer Science in Karpagam University, Coimbatore .India.
2V.Sindhu, pursuing M.E. Computer Science in Karpagam University, Coimbatore .India
2P.S.Balamurugan, pursuing his Doctoral degree at Anna University, Coimbatore. He is working as Assistant Professor at Karpagam College of Engineering Coimbatore, India.
Manuscript received on February 15, 2012. | Revised Manuscript received on February 20, 2012. | Manuscript published on March 05, 2012. | PP: 243-246 | Volume-2 Issue-1, March 2012. | Retrieval Number: A0448022112/2012©BEIESP
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© The Authors. Published By: 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: In moving object environment, it’s unfeasible for database to track the random object movement and to store the locations of object exactly all the times. The basic issue in case of moving object monitoring is efficiency and privacy. We used a framework for moving object to hide their own identities by execution of probabilistic range monitoring queries. The Privacy-aware monitoring framework for spatial join queries which is flexible, it addresses two issues; such as “efficiency and privacy” in monitoring moving object. Because of blurring exact position of object and increase in unnecessary updates costs it’s not possible to provide accurate result. So, we propose an efficient processing of continuously moving top-k spatial keyword (MkSK) queries over spatial query processing for the problem of privacy aware monitoring framework. This develop an efficient query processing, evaluation and reevaluation based on spatial queries which could be effective for computing safe zones that guarantee correct results until the user remains in safe zone, the reported results will be valid and no limiting of frequent updates from objects. The Voronoi Cell Optimization technique which accelerates depth sorting by clustering polygon has been implemented. Our solution is common for moving queries employ safe zones. In our performance study, we compare it with an existing approach using simulation. Our proposed approach outperforms than the conventional approaches without compromising much on the concept of safe zone to save computation and communication costs.
Keywords: Nearest-neighbor queries; probabilistic queries; range queries; spatial databases