Degree Type

Thesis

Date of Award

2011

Degree Name

Master of Science

Department

Industrial and Manufacturing Systems Engineering

First Advisor

Sigurdur Olafsson

Abstract

Mining is one of the most dangerous industries. Mine Safety and Health Administration (MSHA) maintains a database that records thousands of mining related accidents, injuries or illnesses every year with incident descriptions in narrative texts. How to uncover knowledge from these narrative texts is lacking. The goal of this study is to propose a new data mining methodology that incorporates or extends existing methods and is able to uncover useful information from massive amount of narrative texts in a streamline fashion. In our experimentation with data of 2008, we focus on 3 different types of common injuries and apply the new methodology to their narrative texts. Some interesting results are found that are worthy further investigations with the help of mining safety experts.

Copyright Owner

Xiaoli Yang

Language

en

Date Available

2012-04-30

File Format

application/pdf

File Size

58 pages

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