Data Warehousing and Data Quality for a Spatial Decision Support System
$52.95 At Amazon
Spiral-bound
Publisher: Storming Media 1997
Language: English
ISBN-10: 1423566556
ISBN-13: 978-1423566557
Product Description
This is a NAVAL POSTGRADUATE SCHOOL MONTEREY CA report procured by the Pentagon and made available for public release. It has been reproduced in the best form available to the Pentagon. It is not spiral-bound, but rather assembled with Velobinding in a soft, white linen cover. The Storming Media report number is A688633. The abstract provided by the Pentagon follows: This research investigates the problems inherent in Decision Support Systems (DSS) that depend on the quality and accuracy of legacy information as the basis for decision making. A Spatial Decision Support System (SDSS) was developed at Naval Postgraduate School to analyze the comparative desirability of Army Reserve Unit locations. The Army Reserve Installation Evaluation System (ARIES) integrates a GIS mapping engine and a decision model solver in a flexible environment that leverages operational legacy database information for decision-making. Data quality problems from legacy sources motivated the development of a data migration plan to transform the source data into an architecture optimized for the ARIES SDSS application. This research developed a prototype Data Migration Tool (DMT) to extract the relevant source data into a centralized repository for the SDSS with an acceptable degree of data quality to support SDSS outcomes. Six data quality attributes were identified: accuracy, completeness, consistency, timeliness, uniqueness, and validity. The ARIES DMT focused on data validity and developed techniques for measuring and enforcing data validity. The DMT also specified individual responsibilities for data administration, development of data retrieval routines, and data quality assessment. Significant system performance enhancements resulted from implementation of the DMT by leveraging the spatial aspects of the underlying repository through geographic queries that efficiently localized subsets of the data files.
