High performance clustering based on the similarity join
Proceedings of the ninth international conference on Information and knowledge management
Epsilon grid order: an algorithm for the similarity join on massive high-dimensional data
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Dataflow query execution in a parallel main-memory environment
PDIS '91 Proceedings of the first international conference on Parallel and distributed information systems
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
High Dimensional Similarity Joins: Algorithms and Performance Evaluation
IEEE Transactions on Knowledge and Data Engineering
High-Dimensional Similarity Joins
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
High Dimensional Similarity Joins: Algorithms and Performance Evaluation
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
The X-tree: An Index Structure for High-Dimensional Data
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Hash-Merge Join: A Non-blocking Join Algorithm for Producing Fast and Early Join Results
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
RPJ: producing fast join results on streams through rate-based optimization
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
SSDBM '06 Proceedings of the 18th International Conference on Scientific and Statistical Database Management
Fast similarity join for multi-dimensional data
Information Systems
Progressive merge join: a generic and non-blocking sort-based join algorithm
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
RRPJ: result-rate based progressive relational join
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
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The Rate-Based Progressive Join (RPJ) is a nonblocking relational equijoin algorithm. It is an equijoin that can deliver results progressively. In this paper, we first present a naive extension, called neRPJ, to the progressive computation of the similarity join of highdimensional data. We argue that this naive extension is not suitable. We therefore propose an adequate solution in the form of a Result-Rate Progressive Join (RRPJ) for high-dimensional distance similarity joins. Using both synthetic and real-life datasets, we empirically show that RRPJ is effective and efficient, and outperforms the naive extension.