Similarity Joins are recognized among the most useful data processing and analysis operations. They retrieve all data pairs whose distances are smaller than a predefined threshold ε. While several standalone implementations have been proposed, very little work has addressed the implementation of Similarity Join as a physical database operator. In this paper, we focus on the study, design and implementation of a Similarity Join database operator for any dataset that lies in a metric space (DBSimJoin). We describe the changes in each query engine module to implement DBSimJoin and provide details of our implementation in PostgreSQL. The extensive performance evaluation shows that DBSimJoin significantly outperforms alternative approaches. © 2013 Springer-Verlag.
CITATION STYLE
Silva, Y. N., Pearson, S. S., & Cheney, J. A. (2013). Database similarity join for metric spaces. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8199 LNCS, pp. 266–279). https://doi.org/10.1007/978-3-642-41062-8_27
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