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LSF-Join: Locality Sensitive Filtering for Distributed All-Pairs Set Similarity Under Skew
All-pairs set similarity is a widely used data mining task, even for lar...
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Bitmap Filter: Speeding up Exact Set Similarity Joins with Bitwise Operations
The Exact Set Similarity Join problem aims to find all similar sets betw...
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Scalable and robust set similarity join
Set similarity join is a fundamental and well-studied database operator....
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Similarity Join and Similarity Self-Join Size Estimation in a Streaming Environment
We study the problem of similarity self-join and similarity join size es...
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Similarity Join and Self-Join Size Estimation in a Streaming Environment
We study the problem of similarity self-join and similarity join size es...
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Efficient Joinable Table Discovery in Data Lakes: A High-Dimensional Similarity-Based Approach
Finding joinable tables in data lakes is key procedure in many applicati...
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GB-KMV: An Augmented KMV Sketch for Approximate Containment Similarity Search
In this paper, we study the problem of approximate containment similarit...
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Efficient Taxonomic Similarity Joins with Adaptive Overlap Constraint
A similarity join aims to find all similar pairs between two collections of records. Established approaches usually deal with synthetic differences like typos and abbreviations, but neglect the semantic relations between words. Such relations, however, are helpful for obtaining high-quality joining results. In this paper, we leverage the taxonomy knowledge (i.e., a set of IS-A hierarchical relations) to define a similarity measure which finds semantic-similar records from two datasets. Based on this measure, we develop a similarity join algorithm with prefix filtering framework to prune away irrelevant pairs effectively. Our technical contribution here is an algorithm that judiciously selects critical parameters in a prefix filter to maximise its filtering power, supported by an estimation technique and Monte Carlo simulation process. Empirical experiments show that our proposed methods exhibit high efficiency and scalability, outperforming the state-of-art by a large margin.
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