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
The SR-tree: an index structure for high-dimensional nearest neighbor queries
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
The pyramid-technique: towards breaking the curse of dimensionality
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Multidimensional access methods
ACM Computing Surveys (CSUR)
Storing a collection of polygons using quadtrees
ACM Transactions on Graphics (TOG)
Data structures and algorithms for nearest neighbor search in general metric spaces
SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
The Grid File: An Adaptable, Symmetric Multikey File Structure
ACM Transactions on Database Systems (TODS)
Distance browsing in spatial databases
ACM Transactions on Database Systems (TODS)
Indexing the edges—a simple and yet efficient approach to high-dimensional indexing
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
ACM Computing Surveys (CSUR)
Quadtree and R-tree indexes in oracle spatial: a comparison using GIS data
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
The TV-tree: an index structure for high-dimensional data
The VLDB Journal — The International Journal on Very Large Data Bases - Spatial Database Systems
STR: A Simple and Efficient Algorithm for R-Tree Packing
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Similarity Indexing with the SS-tree
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
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
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
The A-tree: An Index Structure for High-Dimensional Spaces Using Relative Approximation
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Hilbert R-tree: An Improved R-tree using Fractals
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Benchmarking Spatial Join Operations with Spatial Output
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Indexing High-Dimensional Data for Content-Based Retrieval in Large Databases
DASFAA '03 Proceedings of the Eighth International Conference on Database Systems for Advanced Applications
The Priority R-tree: a practically efficient and worst-case optimal R-tree
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
iDistance: An adaptive B+-tree based indexing method for nearest neighbor search
ACM Transactions on Database Systems (TODS)
Processing distance-based queries in multidimensional data spaces using R-trees
PCI'01 Proceedings of the 8th Panhellenic conference on Informatics
Performance Comparison of the {\rm R}^{\ast}-Tree and the Quadtree for kNN and Distance Join Queries
IEEE Transactions on Knowledge and Data Engineering
Kernel bandwidth optimization in spike rate estimation
Journal of Computational Neuroscience
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The quest for processing data in high-dimensional space has resulted in a number of innovative indexing mechanisms. Choosing an appropriate indexing method for a given set of data requires careful consideration of data properties, data construction methods, and query types. We present a new indexing method to support efficient point queries, range queries, and k-nearest neighbor queries. Our method indexes objects dynamically using algebraic techniques, and it can substantially reduce the negative impacts of the "curse of dimensionality". In particular, our method partitions the data space recursively into hypercubes of certain capacity and labels each hypercube using the Cantor pairing function, so that all objects in the same hypercube have the same label. The bijective property and the computational efficiency of the Cantor pairing function make it possible to efficiently map between high-dimensional vectors and scalar labels. The partitioning and labeling process splits a subspace if the data items contained in it exceed its capacity. From the data structure point of view, our method constructs a tree where each parent node contains a number of labels and child pointers, and we call it a PL-tree . We compare our method with popular indexing algorithms including R*-tree, X-tree, quad-tree, and iDistance. Our numerical results show that the dynamic PL-tree indexing significantly outperforms the existing indexing mechanisms.