SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Optimal multi-step k-nearest neighbor search
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Distance browsing in spatial databases
ACM Transactions on Database Systems (TODS)
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
FALCON: Feedback Adaptive Loop for Content-Based Retrieval
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Fast Nearest Neighbor Search in Medical Image Databases
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Depth-First K-Nearest Neighbor Finding Using the MaxNearestDist Estimator
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Improvements in Distance-Based Indexing
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
The Amsterdam Library of Object Images
International Journal of Computer Vision
Aggregate nearest neighbor queries in spatial databases
ACM Transactions on Database Systems (TODS)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Aggregate similarity queries in relevance feedback methods for content-based image retrieval
Proceedings of the 2008 ACM symposium on Applied computing
Flexible aggregate similarity search
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Adaptive parallel approximate similarity search for responsive multimedia retrieval
Proceedings of the 20th ACM international conference on Information and knowledge management
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A similarity query considers an element as the query center and searches a dataset to find either the elements far up to a bounding radius or the k nearest ones from the query center. Several algorithms have been developed to efficiently execute similarity queries. However, there are queries that require more than one center, which we call Aggregate Similarity Queries. Such queries appear when the user gives multiple desirable examples, and requests data elements that are similar to all of the examples, as in the case of applying relevance feedback. Here we give the first algorithms that can handle aggregate similarity queries on Metric Access Methods (MAM) such as the M-tree and Slim-tree. Our method, which we call Metric Aggregate Similarity Search (MASS) has the following properties: (a) it requires only the triangle inequality property; (b) it guarantees no false-dismissals, as we prove that it lower-bounds the aggregate distance scores; (c) it can work with any MAM; (d) it can handle any number of query centers, which are either scattered all over the space or concentrated on a restricted region. Experiments on both real and synthetic data show that our method scales on both the number of elements and, if the dataset is in a spatial domain, also on its dimensionality. Moreover, it achieves better results than previous related methods.