JSM 2004 - Toronto

Abstract #300186

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Activity Number: 141
Type: Invited
Date/Time: Monday, August 9, 2004 : 2:00 PM to 3:50 PM
Sponsor: General Methodology
Abstract - #300186
Title: Mining Massive Report Data and Developing Tracking Statistics
Author(s): Daniel R. Jeske and Regina Y. Liu*+
Companies: University of California, Riverside and Rutgers University
Address: Dept. of Statistics, Piscataway, NJ, 08854-8019,
Keywords: text-mining ; report data ; risk indicators ; test classification ; tracking statistics ; error measurements
Abstract:

We outline a systematic data-mining procedure for exploring large free-style text datasets to discover useful features and develop tracking statistics, generally referred to as performance measures or risk indicators. The procedure includes text mining, risk analysis, classification for error measurements and nonparametric multivariate analysis. Two aviation safety report repositories from the FAA and the NTSB will be used to illustrate applications of our research to aviation risk management and general decision-support systems. Some specific text analysis methodologies and tracking statistics will be discussed. Approaches to incorporating misclassified data or error measurements into tracking statistics will be discussed as well.


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Revised March 2004