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A Model of Error Propagation in Conjunctive Decisions and its Application to Database Quality Management

A Model of Error Propagation in Conjunctive Decisions and its Application to Database Quality Management

Irit Askira Gelman
Copyright: © 2012 |Volume: 23 |Issue: 1 |Pages: 24
ISSN: 1063-8016|EISSN: 1533-8010|EISBN13: 9781466614932|DOI: 10.4018/jdm.2012010105
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MLA

Gelman, Irit Askira. "A Model of Error Propagation in Conjunctive Decisions and its Application to Database Quality Management." JDM vol.23, no.1 2012: pp.103-126. http://doi.org/10.4018/jdm.2012010105

APA

Gelman, I. A. (2012). A Model of Error Propagation in Conjunctive Decisions and its Application to Database Quality Management. Journal of Database Management (JDM), 23(1), 103-126. http://doi.org/10.4018/jdm.2012010105

Chicago

Gelman, Irit Askira. "A Model of Error Propagation in Conjunctive Decisions and its Application to Database Quality Management," Journal of Database Management (JDM) 23, no.1: 103-126. http://doi.org/10.4018/jdm.2012010105

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Abstract

This study centers on the accuracy dimension of information quality and models the relationship between input accuracy and output accuracy in a popular class of applications. These applications consist of dichotomous decisions that are implemented through conjunction of selected criteria. In particular, the model provides the tools for quantifying the effect of errors in each decision input on the accuracy of a decision. Application of the model is illustrated through the example of a residential real estate database, where users’ preferences are captured by conjunctive decision rules. This example demonstrates how the new model can be utilized for quantifying the aggregate damage that errors in different database attributes inflict on property selection decisions. Finally, this paper reports on an initial empirical validation of the proposed model through a series of Monte Carlo simulations. Numerical estimates of the model that have been developed through this inquiry can be useful for data and information quality assessments or policy-making purposes. Mainly, they can be integrated into cost-benefit analyses that assess alternative data accuracy enhancements or process and system designs.

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