CORDS: automatic discovery of correlations and soft functional dependencies

By Ihab F. Ilyas, Volker Markl, Peter J. Haas, Paul Geoffrey Brown, Ashraf Aboulnaga. ACM SIGMOD Conference, 2004

367 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/34b088b6ad065a9d4076ca5f2f76b3e18f3632e6

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In Computer Networks and Communications

In Information Systems

In Signal Processing

Citations in the corpus

KATARA: A Data Cleaning System Powered by Knowledge Bases and Crowdsourcing (2015, methodology): “If the the number of attributes is small, we show them all; otherwise, we use off-the-shelf technology to identify attributes that are related to the ones in the question [23].”
Management of probabilistic data: foundations and challenges (2007, background): “A second interpretation is that of soft constraints [51, 73], for example: “each employee has a unique name with probability 0.”
Discovering Denial Constraints (2013, background): “examined the statistical correlations for each column pair to discover soft FDs [18].”
Aurum: A Data Discovery System (2018, result): “For example, work on ﬁnding functional dependencies [27], as well as correlation among columns [28], [29] is complementary to our work.”
Neo: A Learned Query Optimizer (2019, neutral): “In follow-up work, CORDS proactively discovered correlations between any two columns using data samples in advance of query execution [17].”
Interactive and Deterministic Data Cleaning (2016, methodology): “We adopt the techniques proposed in CORDS [29] to profile a database T of relation R.”
How Good Are Query Optimizers, Really? (2015, background): “certain correlations [19] to subsequently create multi-column histograms [34] for these.”
Profiling relational data: a survey (2015, methodology): “Such dependencies are often discovered using sampling [76] or other summarization techniques [31].”
Differentially Private High-Dimensional Data Publication via Sampling-Based Inference (2015, background): “Previous work [13] has indicated that the sample size required for learning correlations for a specified degree of accuracy can be independent of the dataset Algorithm 1 Generate Dependency Graph”