An algorithm for finding nearest neighbours in (approximately) constant average time
Pattern Recognition Letters
Vorono trees and clustering problems
Information Systems
ACM Computing Surveys (CSUR)
Slim-Trees: High Performance Metric Trees Minimizing Overlap Between Nodes
EDBT '00 Proceedings of the 7th International Conference on Extending Database Technology: Advances in Database Technology
Similarity Search without Tears: The OMNI Family of All-purpose Access Methods
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M-tree: An Efficient Access Method for Similarity Search in Metric Spaces
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Near Neighbor Search in Large Metric Spaces
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Searching in metric spaces by spatial approximation
The VLDB Journal — The International Journal on Very Large Data Bases
iDistance: An adaptive B+-tree based indexing method for nearest neighbor search
ACM Transactions on Database Systems (TODS)
Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling)
A Data Structure and an Algorithm for the Nearest Point Problem
IEEE Transactions on Software Engineering
Dynamic spatial approximation trees
Journal of Experimental Algorithmics (JEA)
SISAP '08 Proceedings of the First International Workshop on Similarity Search and Applications (sisap 2008)
Dynamic Spatial Approximation Trees for Massive Data
SISAP '09 Proceedings of the 2009 Second International Workshop on Similarity Search and Applications
DSACL+-tree: a dynamic data structure for similarity search in secondary memory
SISAP'12 Proceedings of the 5th international conference on Similarity Search and Applications
Faster construction of ball-partitioning-based metric access methods
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Multi-level clustering on metric spaces using a Multi-GPU platform
Euro-Par'13 Proceedings of the 19th international conference on Parallel Processing
Range query processing on single and multi GPU environments
Computers and Electrical Engineering
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Metric access methods based on hyperplane partitioning have the advantage, compared to the ball partitioning-based ones, that regions do not overlap. The price is less flexibility for controlling the tree shape, especially in the dynamic scenario, that is, upon insertions and deletions of objects. In this paper we introduce a technique called ghost hyperplanes, which enables fully dynamic data structures based on hyperplane partitioning. We apply the technique to Brin's GNAT static index, obtaining a dynamic variant called EGNAT, which in addition we adapt to secondary memory. We show experimentally that the EGNAT is competitive with the M-tree, the baseline for this scenario. We also apply the ghost hyperplane technique to Voronoi trees, obtaining a competitive dynamic structure for main memory.