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Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Using Neural Networks for Addressing Data Quality During the Software Maintenance Process

Using Neural Networks for Addressing Data Quality During the Software Maintenance Process
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Author(s): Abdullah Al-Namlah (Florida Institute of Technology, USA) and Shirley Ann Becker (Northern Arizona University, USA)
Copyright: 2003
Pages: 4
Source title: Information Technology & Organizations: Trends, Issues, Challenges & Solutions
Source Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-59140-066-0.ch001
ISBN13: 9781616921248
EISBN13: 9781466665330

Abstract

The high cost of software maintenance continues to be a great concern for many organizations due to poor data quality that plagues most legacy database systems. It is proposed in this paper that neural net technology be used to accommodate changes in user requirements when data quality is an issue. Neural nets can be trained to identify semantically equivalent data such that source code modifications do not have to be made. A case study is used to illustrate the use of neural nets to replace source code in identifying duplicate data within and across databases even when data is incorrect or incomplete.

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