TANE: An efficient algorithm for discovering functional and approximate dependencies. environments. The discovery of functional dependencies from relations is an important database analysis technique. We present TANE, an efficient algorithm for finding functional dependencies from large databases. TANE is based on partitioning the set of rows with respect to their attribute values, which makes testing the validity of functional dependencies fast even for a large number of tuples. The use of partitions also makes the discovery of approximate functional dependencies easy and efficient and the erroneous or exceptional rows can be identified easily. Experiments show that TANE is fast in practice. For benchmark databases the running times are improved by several orders of magnitude over previously published results. The algorithm is also applicable to much larger datasets than the previous methods

References in zbMATH (referenced in 39 articles , 1 standard article )

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  1. Combi, Carlo; Rizzi, Romeo; Sala, Pietro: Checking sets of pure evolving association rules (2021)
  2. Atencia, Manuel; David, Jérôme; Euzenat, Jérôme; Napoli, Amedeo; Vizzini, Jérémy: Link key candidate extraction with relational concept analysis (2020)
  3. Caruccio, Loredana; Deufemia, Vincenzo; Polese, Giuseppe: Mining relaxed functional dependencies from data (2020)
  4. Baixeries, Jaume; Codocedo, Victor; Kaytoue, Mehdi; Napoli, Amedeo: Characterizing approximate-matching dependencies in formal concept analysis with pattern structures (2018)
  5. Chardin, Brice; Coquery, Emmanuel; Pailloux, Marie; Petit, Jean-Marc: RQL: a query language for rule discovery in databases (2017)
  6. Combi, Carlo; Sala, Pietro: Mining approximate interval-based temporal dependencies (2016)
  7. Kim, Mijung; Candan, K. Selçuk: Decomposition-by-normalization (DBN): leveraging approximate functional dependencies for efficient CP and Tucker decompositions (2016)
  8. Garnaud, Eve; Maabout, Sofian; Mosbah, Mohamed: Functional dependencies are helpful for partial materialization of data cubes (2015)
  9. Baixeries, Jaume; Kaytoue, Mehdi; Napoli, Amedeo: Characterizing functional dependencies in formal concept analysis with pattern structures (2014)
  10. Cambazard, Hadrien; O’Sullivan, Barry: Erratum to “Reformulating table constraints using functional dependencies---an application to explanation generation” (2010)
  11. Medina, Raoul; Nourine, Lhouari: Conditional functional dependencies: an FCA point of view (2010)
  12. de Marchi, Fabien; Lopes, Stéphane; Petit, Jean-Marc: Unary and n-ary inclusion dependency discovery in relational databases (2009) ioport
  13. Haas, Peter J.; Ilyas, Ihab F.; Lohman, Guy M.; Markl, Volker: Discovering and exploiting statistical properties for query optimization in relational databases: a survey (2009)
  14. Jaudoin, H.; Flouvat, F.; Petit, J.-M.; Toumani, F.: Towards a scalable query rewriting algorithm in presence of value constraints (2009)
  15. Marchi, Fabien De; Lopes, Stéphane; Petit, Jean-Marc: Unary and n-ary inclusion dependency discovery in relational databases (2009) ioport
  16. Medina, Raoul; Nourine, Lhouari: A unified hierarchy for functional dependencies, conditional functional dependencies and association rules (2009)
  17. Naidenova, K. A.: Reducing one class of machine learning algorithms to logical operations of plausible reasoning (2009)
  18. Wolf, Garrett; Kalavagattu, Aravind; Khatri, Hemal; Balakrishnan, Raju; Chokshi, Bhaumik; Fan, Jianchun; Chen, Yi; Kambhampati; Subbarao: Query processing over incomplete autonomous databases: query rewriting using learned data dependencies (2009) ioport
  19. Cambazard, Hadrien; O’Sullivan, Barry: Reformulating table constraints using functional dependencies-an application to explanation generation (2008)
  20. Sánchez, Daniel; Serrano, José-María; Blanco, Ignacio; Martín-Bautista, Maria J.; Miranda, María Amparo Vila: Using association rules to mine for strong approximate dependencies. (2008) ioport

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