Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
A cost model for nearest neighbor search in high-dimensional data space
PODS '97 Proceedings of the sixteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
The pyramid-technique: towards breaking the curse of dimensionality
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Selectivity estimation in spatial databases
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Some approaches to best-match file searching
Communications of the ACM
ACM Computing Surveys (CSUR)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Perceptual Metrics for Image Database Navigation
Perceptual Metrics for Image Database Navigation
Efficient Cost Models for Spatial Queries Using R-Trees
IEEE Transactions on Knowledge and Data Engineering
On the 'Dimensionality Curse' and the 'Self-Similarity Blessing'
IEEE Transactions on Knowledge and Data Engineering
Fast Indexing and Visualization of Metric Data Sets using Slim-Trees
IEEE Transactions on Knowledge and 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
Similarity Search in High Dimensions via Hashing
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Contrast Plots and P-Sphere Trees: Space vs. Time in Nearest Neighbour Searches
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Approximate similarity retrieval with M-trees
The VLDB Journal — The International Journal on Very Large Data Bases
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Distance Exponent: A New Concept for Selectivity Estimation in Metric Trees
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
A Sampling-Based Estimator for Top-k Query
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Index-driven similarity search in metric spaces (Survey Article)
ACM Transactions on Database Systems (TODS)
Probabilistic proximity searching algorithms based on compact partitions
Journal of Discrete Algorithms - SPIRE 2002
IEEE Transactions on Knowledge and Data Engineering
Selectivity estimators for multidimensional range queries over real attributes
The VLDB Journal — The International Journal on Very Large Data Bases
Approximate similarity search: A multi-faceted problem
Journal of Discrete Algorithms
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
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Algorithms to query large sets of simple data (composed of numbers and small character strings) are constructed to retrieve the exact answer, retrieving every relevant element, so the answer said to be exact. Similarity searching over complex data is much more expensive than searching over simple data. Moreover, comparison operations over complex data usually consider features extracted from each element, instead of the elements themselves. Thus, even if an algorithm retrieves an exact answer, it is 'exact' regarding the extracted features, not regarding the original elements themselves. Therefore, trading exact answering with query time response can be worthwhile. In this work we developed two search strategies based on genetic algorithms to allow retrieving approximate data indexed by Metric Access Methods (MAM) within a limited, user-defined, amount of time. These strategies allow implementing algorithms to answer both range and k-nearest neighbor queries, and allow also to estimate the precision obtained for the approximate answer. Experimental evaluation shows that very good results (corresponding to what the user would expect) can be obtained in a fraction of the time required to obtain the exact answer.