Efficient exact set-similarity joins

By Arvind Arasu, Venkatesh Ganti, Raghav Kaushik. Very Large Data Bases Conference, 2006

498 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/08c159feb9c9a94535f64905c961a54586e1d80b

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Citations in the corpus

Large-Scale Deduplication with Constraints Using Dedupalog (2009, background): “, efficiently identify pairs of records that are similar to each other on a specified set of attributes [29], [39], [40], [41].”
Management of probabilistic data: foundations and challenges (2007, background): “This is a central problem in data cleaning and data integration, and has also been called record linkage, deduplication, or merge-purge [34, 3, 13, 37, 43, 49, 15, 80, 7].”
Efficient parallel set-similarity joins using MapReduce (2010, methodology): “Another approach on finding RID pairs of similar records is to use existing set-similarity join algorithms from the literature [23, 3, 4, 29].”
Frameworks for entity matching: A comparison (2010, background): “Fr am ew or k [1 6]”
On indexing error-tolerant set containment (2010, methodology): “been proposed [1, 6, 10, 15, 29, 31].”
Efficient Merging and Filtering Algorithms for Approximate String Searches (2008, methodology): “Other related studies include [1], [2], [10], [9], [12], [13] on similarity set joins.”
Scaling Up All Pairs Similarity Search (background): “The all-pairs similarity search problem has also been addressed in the database community, where it is known as the similarity join problem [1] [7] [21].”
Efficient similarity joins for near duplicate detection (2008, methodology): “Recent advances include inverted index-based methods [24], prefix filtering-based methods [8, 3] and signature-based methods [1].”