ACM Computing Surveys (CSUR)
Processing Complex Similarity Queries with Distance-Based Access Methods
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Similarity Search: The Metric Space Approach (Advances in Database Systems)
Similarity Search: The Metric Space Approach (Advances in Database Systems)
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The efficient similarity search in metric spaces is usually based on several low-level partitioning principles, which allow filtering of non-relevant objects during the search. In this paper, we propose a parameterizable partitioning method based on the generalized hyperplane partitioning (GHP), which utilizes a parameter to adjust "borders" of the partitions. The new partitioning method could be employed in the existing metric indexes that are based on GHP (e.g., GNAT, M-index). Moreover, we could employ the parameterizable GHP in the role of a new multi-example query type, defined as a partition determined by an available query object and several "anti-example" objects. We believe that both applications of parameterizable GHP can soon take place in metric access methods and new query models.