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Efficient approximate top-k mutual information based feature selection. J Intell Inf Syst 2022. [DOI: 10.1007/s10844-022-00750-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Amshakala K, Nedunchezhian R, Rajalakshmi M. Extracting Functional Dependencies in Large Datasets Using MapReduce Model. INTERNATIONAL JOURNAL OF INTELLIGENT INFORMATION TECHNOLOGIES 2014. [DOI: 10.4018/ijiit.2014070102] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Over the last few years, data are generated in large volume at a faster rate and there has been a remarkable growth in the need for large scale data processing systems. As data grows larger in size, data quality is compromised. Functional dependencies representing semantic constraints in data are important for data quality assessment. Executing functional dependency discovery algorithms on a single computer is hard and laborious with large data sets. MapReduce provides an enabling technology for large scale data processing. The open-source Hadoop implementation of MapReduce has provided researchers a powerful tool for tackling large-data problems in a distributed manner. The objective of this study is to extract functional dependencies between attributes from large datasets using MapReduce programming model. Attribute entropy is used to measure the inter attribute correlations, and exploited to discover functional dependencies hidden in the data.
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Affiliation(s)
- K. Amshakala
- Department of CSE and IT, Coimbatore Institute of Technology, Coimbatore, India
| | - R. Nedunchezhian
- Department of CSE, Sri Ranganathar Institute of Engineering and Technology, Coimbatore, India
| | - M. Rajalakshmi
- Department of CSE and IT, Coimbatore Institute of Technology, Coimbatore, India
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Marchi FD, Lopes S, Petit JM. Unary and n-ary inclusion dependency discovery in relational databases. J Intell Inf Syst 2008. [DOI: 10.1007/s10844-007-0048-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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De Marchi F, Petit JM. Semantic sampling of existing databases through informative Armstrong databases. INFORM SYST 2007. [DOI: 10.1016/j.is.2005.12.007] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Giannella C, Robertson E. A note on approximation measures for multi-valued dependencies in relational databases. INFORM PROCESS LETT 2003. [DOI: 10.1016/s0020-0190(02)00365-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Lopes S, Petit JM, Lakhal L. Functional and approximate dependency mining: database and FCA points of view. J EXP THEOR ARTIF IN 2002. [DOI: 10.1080/09528130210164143] [Citation(s) in RCA: 18] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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