Description
Data mining is increasing in importance in solving a variety of industry problems. Our initiative involves the estimation of resource requirements by skill set for future projects by mining and analyzing actual resource consumption data from past projects in the semiconductor industry. To achieve this goal we face difficulties like data with relevant consumption information but stored in different format and insufficient data about project attributes to interpret consumption data. Our first goal is to clean the historical data and organize it into meaningful structures for analysis. Once the preprocessing on data is completed, different data mining techniques like clustering is applied to find projects which involve resources of similar skillsets and which involve similar complexities and size. This results in "resource utilization templates" for groups of related projects from a resource consumption perspective. Then project characteristics are identified which generate this diversity in headcounts and skillsets. These characteristics are not currently contained in the data base and are elicited from the managers of historical projects. This represents an opportunity to improve the usefulness of the data collection system for the future. The ultimate goal is to match the product technical features with the resource requirement for projects in the past as a model to forecast resource requirements by skill set for future projects. The forecasting model is developed using linear regression with cross validation of the training data as the past project execution are relatively few in number. Acceptable levels of forecast accuracy are achieved relative to human experts' results and the tool is applied to forecast some future projects' resource demand.
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Details
Title
- Industrial applications of data mining: engineering effort forecasting based on mining and analysis of patterns in historical project execution data
Contributors
- Bhattacharya, Indrani (Author)
- Sen, Arunabha (Thesis advisor)
- Kempf, Karl G. (Thesis advisor)
- Liu, Huan (Committee member)
- Arizona State University (Publisher)
Date Created
The date the item was original created (prior to any relationship with the ASU Digital Repositories.)
2013
Subjects
Resource Type
Collections this item is in
Note
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thesisPartial requirement for: M.S., Arizona State University, 2013
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bibliographyIncludes bibliographical references (p. 71-72)
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Field of study: Computer science
Citation and reuse
Statement of Responsibility
by Indrani Bhattacharya