Ranking Spatial Data by Quality Preferences
Shadeeda Nalakath1, Sreeja Rajesh2
1Shadeeda Nalakath, Department of Computer Science, M.G. University, Kottayam, Kerala, India.
2Sreeja Rajesh, Department of Computer Science & Engineering, SCMS School of Engineering & Technology, Kerala, India.
Manuscript received on November 22, 2013. | Revised Manuscript received on December 16, 2013. | Manuscript published on December 30, 2013. | PP: 172-176 | Volume-3, Issue-2, December 2013. | Retrieval Number: B2391123213/2013©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: The objects in real world can be ranked based on the features in their spatial neighborhood using a preference based top-k special query. In this paper, a two purpose query structure for satisfying the user requirements is implemented. For example, a user who wishes to find a hotel with 3 star categories that serves sea food which provides the nearest airport facility. This concept can be obtained by developing a system that takes a particular query as the input and displays a ranked set of top k best objects that satisfy user requirements. For that, an indexing technique R-tree and a search method BB algorithm for efficiently processing top-k spatial preference query is used. R-tree (Real-tree), a data structure is the first index specifically designed to handle multidimensional extended objects and branch and bound (BB) algorithm that makes searching easier, faster and accurate. The key idea is to compute upper bound scores for non-leaf entries in the object tree, and prunes those that cannot lead to better results. The advantage of using this algorithm is that it can reduce the number of steps to be examined.
Keywords: Query processing, Spatial databases, R-tree.