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HST2010

Abstract:

Industrial methods for quality analysis massively rely on structured data describing product features and product usage. The analysis of such data is normally done using complex reporting or sophisticated data mining methods. Besides this structured data, companies very often also posses large amounts of unstructured text like call center reports, internet
fora or repair order documents. Despite the rising interest in text mining applications for industrial usage, the uncertainty about the real benefits is still high.

In this work, we will present a comparison of the usage of structured versus unstructured data on two quality analysis use cases: Early warning and the detection of repeat repairs.

Type: Inproceedings

Author: Hänig, C. and Schierle, M. and Trabold, D.
Title: Comparison of Structured vs. Unstructured Data for Industrial Quality Analysis
Booktitle: Proceedings of the World Congress on Engineering and Computer Science 2010 Vol I (WCECS 2010)
Year: 2010
Pages:432--438
Publisher:IAENG
Address:
Note:Best Paper Award of International Conference on Machine Learning and Data Analysis 2010
@INPROCEEDINGS{HST2010,
AUTHOR = {Hänig, C. and Schierle, M. and Trabold, D.},
TITLE = {Comparison of Structured vs. Unstructured Data for Industrial Quality Analysis},
BOOKTITLE = {Proceedings of the World Congress on Engineering and Computer Science 2010 Vol I (WCECS 2010)},
YEAR = {2010},
PAGES = {432--438},
PUBLISHER = {IAENG},
NOTE = {Best Paper Award of International Conference on Machine Learning and Data Analysis 2010}
}