On the use of machine-assisted knowledge discovery to analyze and reengineer measurement frameworks

Authors:
Inderpal S. Bhandari;Manoel G. Mendonca;Jack Dawson
Affiliations:
IBM T.J. Watson;Univeristy of Maryland;IBM Toronto
Venue:
CASCON '95 Proceedings of the 1995 conference of the Centre for Advanced Studies on Collaborative research
Year:
1995

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Abstract

We call the set of metrics, data collection mechanisms, and measurement models used by organizations in running their businesses a Measurement Framework. This paper [1] describes how a knowledge discovery technique called Attribute Focusing (AF) can be combined with a measurement planning approach called the Goal/Question/Metric Paradigm (GQM) to analyze and reengineer the Measurement Framework of an organization. The GQM Paradigm is widely used by the software engineering community to handle Measurement Frameworks in a top-down, goal-oriented fashion. The AF technique is a machine-assisted knowledge discovery technique which has been widely used to help domain experts search for knowledge in a database of measurement (attribute-valued) data. Using our experience analyzing Software Customer Satisfaction survey data at IBM, we illustrate how the AF Technique can be combined with GQM to improve a Measurement Framework. We argue that this may be a good approach to reengineering and improving existing Measurement Frameworks.