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Industrial applications of data mining: engineering effort forecasting based on mining and analysis of patterns in historical project execution data

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 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.
ContributorsBhattacharya, Indrani (Author) / Sen, Arunabha (Thesis advisor) / Kempf, Karl G. (Thesis advisor) / Liu, Huan (Committee member) / Arizona State University (Publisher)
Created2013
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Value Analysis of Microsoft's Game Pass

Description
The retail cost of video games has remained fairly consistent over the decades as the industry has grown so significantly. Emerging alternatives to buying individual games, such as subscription services, attempt to provide a better deal than the current options. Examining the various attributes that all video games possess, regression…

The retail cost of video games has remained fairly consistent over the decades as the industry has grown so significantly. Emerging alternatives to buying individual games, such as subscription services, attempt to provide a better deal than the current options. Examining the various attributes that all video games possess, regression analysis can be performed to look for what factors may impact the retail cost of a game. After performing the analysis, however, the low adjusted R-square values indicate that the analysis only accounts for a small percentage of the retail cost variability. This suggests that the chosen attributes are not reliable in estimating retail cost with a regression analysis.
ContributorsKeller, Caden Matthew (Author) / Michael, Clough (Thesis director) / Juarez, Joseph (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Dean, W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
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mHealth Patient Care Improvement Study Through Statistical Analysis

Description
Technological applications are continually being developed in the healthcare industry as technology becomes increasingly more available. In recent years, companies have started creating mobile applications to address various conditions and diseases. This falls under mHealth or the “use of mobile phones and other wireless technology in medical care” (Rouse, 2018).…

Technological applications are continually being developed in the healthcare industry as technology becomes increasingly more available. In recent years, companies have started creating mobile applications to address various conditions and diseases. This falls under mHealth or the “use of mobile phones and other wireless technology in medical care” (Rouse, 2018). The goal of this study was to identify if data gathered through the use of mHealth methods can be used to build predictive models. The first part of this thesis contains a literature review presenting relevant definitions and several potential studies that involved the use of technology in healthcare applications. The second part of this thesis focuses on data from one study, where regression analysis is used to develop predictive models.

Rouse, M. (2018). mHealth (mobile health). Retrieved from https://searchhealthit.techtarget.com/definition/mHealth
ContributorsAkers, Lindsay (Co-author) / Kiraly, Alyssa (Co-author) / Li, Jing (Thesis director) / Yoon, Hyunsoo (Committee member) / Industrial, Systems & Operations Engineering Prgm (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05