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
Voronoi diagrams—a survey of a fundamental geometric data structure
ACM Computing Surveys (CSUR)
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 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
Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
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
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Indexing the Distance: An Efficient Method to KNN Processing
Proceedings of the 27th International Conference on Very Large Data Bases
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Making the Pyramid Technique Robust to Query Types and Workloads
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Towards effective indexing for very large video sequence database
Proceedings of the 2005 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)
Location-Dependent Queries in Mobile Contexts: Distributed Processing Using Mobile Agents
IEEE Transactions on Mobile Computing
Using high dimensional indexes to support relevance feedback based interactive images retrieval
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Peer-to-peer similarity search in metric spaces
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
iDistance Based Interactive Visual Surveillance Retrieval Algorithm
ICICTA '08 Proceedings of the 2008 International Conference on Intelligent Computation Technology and Automation - Volume 01
Quality and efficiency in high dimensional nearest neighbor search
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
SIMP: accurate and efficient near neighbor search in high dimensional spaces
Proceedings of the 15th International Conference on Extending Database Technology
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Efficient database indexing and information retrieval tasks such as k-nearest neighbor (kNN) search still remain difficult challenges in large-scale and high-dimensional data. In this work, we perform the first comprehensive analysis of different partitioning strategies for the state-of-the-art high-dimensional indexing technique iDistance. This work greatly extends the discussion of why certain strategies work better than others over datasets of various distributions, dimensionality, and size. Through the use of novel partitioning strategies and extensive experimentation on real and synthetic datasets, our results establish an up-to-date iDistance benchmark for efficient kNN querying of large-scale and high-dimensional data and highlight the inherent difficulties associated with such tasks. We show that partitioning strategies can greatly affect the performance of iDistance and outline current best practices for using the indexing algorithm in modern application or comparative evaluation.