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Approximate membership localization (AML) for web-based join

Authors:

Xiaoyong DuAuthors Info & Claims

CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge management

Pages 1321 - 1324

https://doi.org/10.1145/1871437.1871611

Published: 26 October 2010 Publication History

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Abstract

In this paper, we propose a search-based approach to join two tables in the absence of clean join attributes. Non-structured documents from the web are used to express the correlations between a given query and a reference list. To implement this approach, a major challenge we meet is how to efficiently determine the number of times and the locations of each clean reference from the reference list that is approximately mentioned in the retrieved documents. We formalize the Approximate Membership Localization (AML) problem and propose an efficient partial pruning algorithm to solve it. A study using real-word data sets demonstrates the effectiveness of our search-based approach, and the efficiency of our AML algorithm.

References

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K. Chakrabarti, S. Chaudhuri, V. Ganti, and D. Xin. An efficient filter for approximate membership checking. In Proceedings of the 2008 ACM SIGMOD international conference on Management of data, pages 805--818, 2008.

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Cited By

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Li ZSitbon LWang LZhou XDu X(2013)AMLIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2011.17825:2(298-310)Online publication date: 1-Feb-2013
https://dl.acm.org/doi/10.1109/TKDE.2011.178
li zSitbon LZhou X(2011)Learning-based relevance feedback for web-based relation completionProceedings of the 20th ACM international conference on Information and knowledge management10.1145/2063576.2063796(1535-1540)Online publication date: 24-Oct-2011
https://dl.acm.org/doi/10.1145/2063576.2063796

Index Terms

Approximate membership localization (AML) for web-based join
1. Information systems
  1. Data management systems
    1. Database management system engines

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Information & Contributors

Information

Published In

CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge management

October 2010

2036 pages

ISBN:9781450300995

DOI:10.1145/1871437

General Chair:
Jimmy Huang
York University, Canada
,
Program Chairs:
Nick Koudas
University of Toronto, Canada
,
Gareth Jones
Dublin City University, Ireland
,
Xindong Wu
University of Vermont, USA
,
Kevyn Collins-Thompson
Microsoft Research, USA
,
Aijun An
York University, Canada

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 October 2010

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CIKM '10

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CIKM '10: International Conference on Information and Knowledge Management

October 26 - 30, 2010

ON, Toronto, Canada

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

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Li ZSitbon LWang LZhou XDu X(2013)AMLIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2011.17825:2(298-310)Online publication date: 1-Feb-2013
https://dl.acm.org/doi/10.1109/TKDE.2011.178
li zSitbon LZhou X(2011)Learning-based relevance feedback for web-based relation completionProceedings of the 20th ACM international conference on Information and knowledge management10.1145/2063576.2063796(1535-1540)Online publication date: 24-Oct-2011
https://dl.acm.org/doi/10.1145/2063576.2063796

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