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10.1109/ICSPS.2009.133guideproceedingsArticle/Chapter ViewAbstractPublication PagesConference Proceedingsacm-pubtype
Article

Alliance Rules for Data Warehouse Cleansing

Published: 15 May 2009 Publication History

Abstract

Data Cleansing is an activity performed on the data sets of data warehouse to enhance and maintain the quality and consistency of the data. This paper addresses the problems related with dirty data, entrance of dirty data and detection of dirty data in the data warehouse. The paper perceives the procedure of data cleansing from a different perspective. It provides an algorithm for the detection of errors and dirty data in the data sets of an already existing data warehouse. The paper characterizes the alliance rules based on the concept of mathematical association rules to determine the dirty and faulty data in data warehouse. The research marks the use of q-grams [1] to determine the errors in a prominent way.

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  • (2018)Data quality improvement in data warehouseInternational Journal of Data Analysis Techniques and Strategies10.5555/3070815.30708179:1(17-33)Online publication date: 14-Dec-2018
  • (2018)A Hybrid Approach Using Maximum Entropy and Bayesian Learning for Detecting Delinquency in Financial IndustryInternational Journal of Knowledge-Based Organizations10.5555/2890890.28908956:1(60-73)Online publication date: 13-Dec-2018

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Published In

cover image Guide Proceedings
ICSPS '09: Proceedings of the 2009 International Conference on Signal Processing Systems
May 2009
979 pages
ISBN:9780769536545

Publisher

IEEE Computer Society

United States

Publication History

Published: 15 May 2009

Author Tags

  1. data cleansing
  2. data marts
  3. data warehouse

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Cited By

View all
  • (2018)Data quality improvement in data warehouseInternational Journal of Data Analysis Techniques and Strategies10.5555/3070815.30708179:1(17-33)Online publication date: 14-Dec-2018
  • (2018)A Hybrid Approach Using Maximum Entropy and Bayesian Learning for Detecting Delinquency in Financial IndustryInternational Journal of Knowledge-Based Organizations10.5555/2890890.28908956:1(60-73)Online publication date: 13-Dec-2018

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