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Tane: An Efficient Algorithm for Discovering Functional and Approximate Dependencies

Tane: An Efficient Algorithm for Discovering Functional and Approximate Dependencies 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. Received 17 September, 1998. Revised 31 March, 1999. 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Search this journal: Advanced » Current Issue November 2015 58 (11) Alert me to new issues The Journal About this journal Rights & Permissions Dispatch date of the next issue The Wilkes Award We are mobile – find out more Reviewer Guidelines Guidelines for Editors Review and Appeals Process Journals Career Network Published on behalf of BCS, The Chartered Institute for IT Impact factor: 0.787 5-Yr impact factor: 0.962 View full editorial board For Authors Instructions to authors Online submission instructions Submit a manuscript Self archiving policy Alerting Services Email table of contents Email Advance Access CiteTrack XML RSS feed Corporate Services Advertising sales Reprints Supplements var taxonomies = ("SCI01540"); Most Most Read Fast Implementation of Relational Operations Via Inverse Projections What is the Use of Operating Systems? Defining System Requirements to meet Business Needs: a Case Study Example On the Meaning of Safety and Security A Simplex Method for Function Minimization » View all Most Read articles Most Cited A Simplex Method for Function Minimization How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis Hierarchical grouping methods and stopping rules: an evaluation Function minimization by conjugate gradients A new approach to variable metric algorithms » View all Most Cited articles Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department. 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Tane: An Efficient Algorithm for Discovering Functional and Approximate Dependencies

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References (27)

Publisher
Oxford University Press
Copyright
Copyright © 2015 British Computer Society
ISSN
0010-4620
eISSN
1460-2067
DOI
10.1093/comjnl/42.2.100
Publisher site
See Article on Publisher Site

Abstract

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. Received 17 September, 1998. Revised 31 March, 1999. Copyright 1999 « Previous | Next Article » Table of Contents This Article The Computer Journal (1999) 42 (2): 100-111. doi: 10.1093/comjnl/42.2.100 » Abstract Free Full Text (PDF) Free Classifications Article Services Article metrics Alert me when cited Alert me if corrected Find similar articles Similar articles in Web of Science Add to my archive Download citation Request Permissions Citing Articles Load citing article information Citing articles via CrossRef Citing articles via Scopus Citing articles via Web of Science Citing articles via Google Scholar Google Scholar Articles by Huhtala, Y. Articles by Toivonen, H. Search for related content Related Content Load related web page information Share Email this article CiteULike Delicious Facebook Google+ Mendeley Twitter What's this? Search this journal: Advanced » Current Issue November 2015 58 (11) Alert me to new issues The Journal About this journal Rights & Permissions Dispatch date of the next issue The Wilkes Award We are mobile – find out more Reviewer Guidelines Guidelines for Editors Review and Appeals Process Journals Career Network Published on behalf of BCS, The Chartered Institute for IT Impact factor: 0.787 5-Yr impact factor: 0.962 View full editorial board For Authors Instructions to authors Online submission instructions Submit a manuscript Self archiving policy Alerting Services Email table of contents Email Advance Access CiteTrack XML RSS feed Corporate Services Advertising sales Reprints Supplements var taxonomies = ("SCI01540"); Most Most Read Fast Implementation of Relational Operations Via Inverse Projections What is the Use of Operating Systems? Defining System Requirements to meet Business Needs: a Case Study Example On the Meaning of Safety and Security A Simplex Method for Function Minimization » View all Most Read articles Most Cited A Simplex Method for Function Minimization How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis Hierarchical grouping methods and stopping rules: an evaluation Function minimization by conjugate gradients A new approach to variable metric algorithms » View all Most Cited articles Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department. Online ISSN 1460-2067 - Print ISSN 0010-4620 Copyright © 2015 British Computer Society Oxford Journals Oxford University Press Site Map Privacy Policy Cookie Policy Legal Notices Frequently Asked Questions Other Oxford University Press sites: Oxford University Press Oxford Journals China Oxford Journals Japan Academic & Professional books Children's & Schools Books Dictionaries & Reference Dictionary of National Biography Digital Reference English Language Teaching Higher Education Textbooks International Education Unit Law Medicine Music Online Products & Publishing Oxford Bibliographies Online Oxford Dictionaries Online Oxford English Dictionary Oxford Language Dictionaries Online Oxford Scholarship Online Reference Rights and Permissions Resources for Retailers & Wholesalers Resources for the Healthcare Industry Very Short Introductions World's Classics function fnc_onDomLoaded() { var query_context = getQueryContext(); PF_initOIUnderbar(query_context,":QS:default","","JRN"); PF_insertOIUnderbar(0); }; if (window.addEventListener) { window.addEventListener('load', fnc_onDomLoaded, false); } else if (window.attachEvent) { window.attachEvent('onload', fnc_onDomLoaded); } var gaJsHost = (("https:" == document.location.protocol) ? "https://ssl." : "http://www."); document.write(unescape("%3Cscript src='" + gaJsHost + "google-analytics.com/ga.js' type='text/javascript'%3E%3C/script%3E")); try { var pageTracker = _gat._getTracker("UA-189672-16"); pageTracker._setDomainName(".oxfordjournals.org"); pageTracker._trackPageview(); } catch(err) {}

Journal

The Computer JournalOxford University Press

Published: Jan 1, 1999

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