The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Multidimensional binary search trees used for associative searching
Communications of the ACM
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
Range Searching in Categorical Data: Colored Range Searching on Grid
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
Efficient algorithms for reverse proximity query problems
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
Optimal and near-optimal algorithms for generalized intersection reporting on pointer machines
Information Processing Letters
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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.