Colored Range Searching on Internal Memory
Recent advances in various application fields, like GIS, finance and others, has lead to a large increase in both the volume and the characteristics of the data being collected. Hence, general range queries on these datasets are not sufficient enough to obtain good insights and useful information from the data. This leads to the need for more sophisticated queries and hence novel data structures and algorithms such as the orthogonal colored range searching (OCRS) problem which is a generalized version of orthogonal range searching. In this work, an efficient main-memory algorithm has been proposed to solve OCRS by augmenting k-d tree with additional information. The performance of the proposed algorithm has been evaluated through extensive experiments and comparison with two base-line algorithms is presented. The data structure takes up linear or near-linear space of O(n logα), where α is the number of colors in the dataset (α ≤ n). The query response time varies minimally irrespective of the number of colors and the query box size.
KeywordsLeaf Node Computational Geometry Range Query Synthetic Dataset Theoretical Solution
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