Segment Match Refinement and Applications
WABI '02 Proceedings of the Second International Workshop on Algorithms in Bioinformatics
New Algorithms for Subset Query, Partial Match, Orthogonal Range Searching, and Related Problems
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Simple and Efficient Bilayer Cross Counting
GD '02 Revised Papers from the 10th International Symposium on Graph Drawing
On map-matching vehicle tracking data
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Fréchet distance for curves, revisited
ESA'06 Proceedings of the 14th conference on Annual European Symposium - Volume 14
IEEE Transactions on Computers
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Untangled Monotonic Chains and Adaptive Range Search
ISAAC '09 Proceedings of the 20th International Symposium on Algorithms and Computation
Criteria for reducibility of moving objects closeness problem
ADBIS'10 Proceedings of the 14th east European conference on Advances in databases and information systems
Range queries over untangled chains
SPIRE'10 Proceedings of the 17th international conference on String processing and information retrieval
Orthogonal range searching on the RAM, revisited
Proceedings of the twenty-seventh annual symposium on Computational geometry
Untangled monotonic chains and adaptive range search
Theoretical Computer Science
Efficient retrieval of approximate palindromes in a run-length encoded string
Theoretical Computer Science
BALLAST: a ball-based algorithm for structural motifs
RECOMB'12 Proceedings of the 16th Annual international conference on Research in Computational Molecular Biology
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Given a set of points in a d-dimensional space, an orthogonal range query is a request for the number of points in a specified d-dimensional box. We present a data structure and algorithm which enable one to insert and delete points and to perform orthogonal range queries. The worstcase time complexity for n operations is O(n logd n); the space usea is O(n logd-1 n). (O-notation here is with respect to n; the constant is allowed to depend on d.) Next we briefly discuss decision tree bounds on the complexity of orthogonal range queries. We show that a decision tree of height O(dn log n) (Where the implied constant does not depend on d or n) can be constructed to process n operations in d dimensions. This suggests that the standard decision tree model will not provide a useful method for investigating the complexity of such problems.