Paper Info
Title
On the use of machine-assisted knowledge discovery to analyze and reengineer measurement frameworks
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.
Year
Venue
Keywords
1995
CASCON
data collection mechanism,gqm paradigm,existing measurement frameworks,measurement planning approach,measurement model,af technique,reengineer measurement framework,knowledge discovery technique,measurement frameworks,machine-assisted knowledge discovery technique,measurement framework,top down,data collection,goal question metric,software engineering,customer satisfaction,knowledge discovery,survey data,goal orientation
Field
DocType
Citations 
Data science,Data collection,Survey data collection,IBM,Customer satisfaction,GQM,Computer science,Software,Knowledge extraction,Business process reengineering
Conference
2
PageRank 
References 
Authors
0.47
11
3
Name
Order
Citations
PageRank
Inderpal S. Bhandari151762.14
Manoel G. Mendonca220.47
Jack Dawson320.47