Notice of Violation of IEEE Publication Principles - Location Based Hybrid Indexing Structure - R k-d Tree

Abstract

Notice of Violation of IEEE Publication Principles<br><br>"Location Based Hybrid Indexing Structure - R k-d Tree"<br>by P. AnandhaKumar, J.Priyadarshini, C.Monisha, K.Sugirtha, and Sandhya Raghavan<br>in the Proceedings of the First International Conference on Integrated Intelligent Computing, August 2010, pp. 140-145<br><br>After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE’s Publication Principles.<br><br>This paper duplicates significant amounts of text and figures from the paper cited below. The original content was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.<br><br>Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:<br><br>"The Hybrid Tree: An Index Structure for High Dimensional Feature Spaces"<br>by Kaushik Chakrabarti and Sharad Mehrotra<br>in the Proceedings of the 15th International Conference on Data Engineering, January 1999<br><br> <br/> Location based spatial object selection and searching is emerging as an important search paradigm in indexing of multimedia database systems. The technique used is to map the objects as points into a two dimensional space which is indexed using a multidimensional data structure. Although several data structures have been proposed for location and spatial indexing, none of them is known to index points in both overlapping and non-overlapping regions in an optimum query retrieval time. This paper introduces the hybrid tree - a multidimensional data structure for indexing two dimensional location spaces. Unlike other multidimensional data structures, the hybrid tree cannot be classified as either a pure data partitioning (DP) index structure (e.g., R-tree, SS-tree, SR tree) or a pure space partitioning (SP) one (e.g., KD tree, hB tree);rather, it “combines” positive aspects of the two types of index structures a single data structure to achieve search performance is increased by a time complexity of O (nlog(log n)) than either of the above techniques (hence, the name “hybrid”). Our experiments on “real” distributed large size spatial databases demonstrate that the fan out is independent of dimensionality and enables fast intra node search. It significantly outperforms purely k-d tree space partitioning and R-tree data partitioning-based index mechanisms as well as linear scan at all dimensionalities for large sized databases.