An algorithm for finding nearest neighbours in (approximately) constant average time
Pattern Recognition Letters
Vorono trees and clustering problems
Information Systems
Distance-based indexing for high-dimensional metric spaces
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
A cost model for similarity queries in metric spaces
PODS '98 Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
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
A cost model for query processing in high dimensional data spaces
ACM Transactions on Database Systems (TODS)
The choice of reference points in best-match file searching
Communications of the ACM
Some approaches to best-match file searching
Communications of the ACM
ACM Computing Surveys (CSUR)
Fast Nearest-Neighbor Search in Dissimilarity Spaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Proximity Matching Using Fixed-Queries Trees
CPM '94 Proceedings of the 5th Annual Symposium on Combinatorial Pattern Matching
Searching in metric spaces by spatial approximation
The VLDB Journal — The International Journal on Very Large Data Bases
Pivot selection techniques for proximity searching in metric spaces
Pattern Recognition Letters
Probabilistic proximity searching algorithms based on compact partitions
Journal of Discrete Algorithms - SPIRE 2002
Antipole Tree Indexing to Support Range Search and K-Nearest Neighbor Search in Metric Spaces
IEEE Transactions on Knowledge and Data Engineering
Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling)
A compact space decomposition for effective metric indexing
Pattern Recognition Letters
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
A Data Structure and an Algorithm for the Nearest Point Problem
IEEE Transactions on Software Engineering
The Many Facets of Approximate Similarity Search
SISAP '08 Proceedings of the First International Workshop on Similarity Search and Applications (sisap 2008)
Curse of Dimensionality in Pivot Based Indexes
SISAP '09 Proceedings of the 2009 Second International Workshop on Similarity Search and Applications
Dynamic Spatial Approximation Trees for Massive Data
SISAP '09 Proceedings of the 2009 Second International Workshop on Similarity Search and Applications
Indexability, concentration, and VC theory
Proceedings of the Third International Conference on SImilarity Search and APplications
On the asymptotic behavior of nearest neighbor search using pivot-based indexes
Proceedings of the Third International Conference on SImilarity Search and APplications
Improving the similarity search of tandem mass spectra using metric access methods
Proceedings of the Third International Conference on SImilarity Search and APplications
Ptolemaic indexing of the signature quadratic form distance
Proceedings of the Fourth International Conference on SImilarity Search and APplications
Clustered pivot tables for I/O-optimized similarity search
Proceedings of the Fourth International Conference on SImilarity Search and APplications
Proceedings of the Fourth International Conference on SImilarity Search and APplications
Fuzzy approach to non-metric similarity indexing
Proceedings of the Fourth International Conference on SImilarity Search and APplications
Indexability, concentration, and VC theory
Journal of Discrete Algorithms
Pivot selection: Dimension reduction for distance-based indexing
Journal of Discrete Algorithms
Combining CPU and GPU architectures for fast similarity search
Distributed and Parallel Databases
Algorithmic exploration of axiom spaces for efficient similarity search at large scale
SISAP'12 Proceedings of the 5th international conference on Similarity Search and Applications
Efficient indexing of similarity models with inequality symbolic regression
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Universal indexing of arbitrary similarity models
Proceedings of the VLDB Endowment
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It has been a long way since the beginnings of metric space searching, where people coming from algorithmics tried to apply their background to this new paradigm, obtaining variable, but especially difficult to explain, success or lack of it. Since then, some has been learned about the specifics of the problem, in particular regarding key aspects such as the intrinsic dimensionality, that were not well understood in the beginning. The inclusion of those aspects in the picture has led to the most important developments in the area. Similarly, researchers have tried to apply asymptotic analysis concepts to understand and predict the performance of the data structures. Again, it was soon clear that this was insufficient, and that the characteristics of the metric space itself could not be neglected. Although some progress has been made on understanding concepts such as the curse of dimensionality, modern researchers seem to have given up in using asymptotic analysis. They rely on experiments, or at best in detailed cost models that are good predictors but do not explain why the data structures perform in the way they do. In this paper I will argue that this is a big loss. Even if the predictive capability of asymptotic analysis is poor, it constitutes a great tool to understand the algorithmic concepts behind the different data structures, and gives powerful hints in the design of new ones. I will exemplify my view by recollecting what is known on asymptotic analysis of metric indexes, and will add some new results.