Eliminating Fuzzy Duplicates in Data Warehouses

By R. Ananthakrishna, Surajit Chaudhuri, Venkatesh Ganti. Very Large Data Bases Conference, 2002

461 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/e79e12965ae9fd216cb434bb5efde92b16ff4ef4

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Citations in the corpus

Data Cleaning: Overview and Emerging Challenges (2016, methodology): “Current techniques include blocking for duplicate detection [5], sampling for data cleaning [75], and distributed data cleaning [47, 45, 20].”
QuERy: A Framework for Integrating Entity Resolution with Query Processing (2015, background): “The majority of previous ER research has focused on improving either its eﬃciency [16, 21] or quality [5, 7].”
Trends in Cleaning Relational Data: Consistency and Deduplication (2015, unknown): “different blocks are considered non-duplicates [4, 14].”
Large-Scale Collective Entity Matching (2011, unknown): “Simple relational techniques take the attributes of related entities into account when computing the similarity scores of entity pairs [1, 12].”
Efficient Top-k Query Evaluation on Probabilistic Data (2007, background): “There is a rich literature on record linkage (also known as de-duplication, or merge-purge) [1, 4, 9, 11, 14, 15, 22, 23], that offers an excellent collection of techniques for computing these similarity scores.”
A Latent Dirichlet Model for Unsupervised Entity Resolution (2005, background): “Non-probabilistic approaches that take relational features into account for data integration have been proposed [11, 7, 1, 3, 20, 12].”
Progressive Approach to Relational Entity Resolution (2014, background): “Most prior work in this area has focused on improving either the efficiency of the ER algorithms [14,18] or the quality of their results [4,6,24].”
Duplicate Record Detection: A Survey (2007, background): “Ananthakrishna et al. [85] describe a similarity metric that uses not only the textual similarity, but the “co-occurrence” similarity of two entries in a database.”
Reference reconciliation in complex information spaces (2005, methodology): “Attribute comparison is done by using either generic string similarity measures (see [10, 4] for a comprehensive comparison and [6, 35, 3] for recent adaptive approaches) or some domain-speciflc measures (e.g., geographic locality in [ 1 ]).”
Evaluation of entity resolution approaches on real-world match problems (2010, methodology): “Most previous studies used a single match approach like thresholdbased attribute matching (similarity join [5]), clustering [26], or context-based matching [1], [12], [33].”
An Introduction to Duplicate Detection (2010, methodology): “One possibility to exploit the hierarchical structure is to traverse the tree in a top-down fashion [Ananthakrishna et al., 2002; Weis and Naumann, 2004].”
Efficient set joins on similarity predicates (2004, background): “Jaccard coe.cient > f where the goal is to .nd all set pairs where the ratio of the intersect size to union size is greater than a fraction f. Weighted match > T where elements are attached with arbitrary weights (for example, inverse of their frequency in the database) and the goal is to .nd…”