Details
| Original language | English |
|---|---|
| Title of host publication | IEMC-Europe 2008 - 2008 IEEE International Engineering Management Conference, Europe |
| Subtitle of host publication | Managing Engineering, Technology and Innovation for Growth |
| Publication status | Published - 2008 |
| Event | IEMC-Europe 2008 - 2008 IEEE International Engineering Management Conference, Europe: Managing Engineering, Technology and Innovation for Growth - Estori, Portugal Duration: 28 Jun 2008 → 30 Jun 2008 |
Publication series
| Name | IEMC-Europe 2008 - 2008 IEEE International Engineering Management Conference, Europe: Managing Engineering, Technology and Innovation for Growth |
|---|
Abstract
Improving the productivity of any business process initially requires its measurement. Therefore, automated models for comparison, simulation and analysis of products and the appendant workflows are being developed and improved constantly. Since the results delivered by such automatically trained systems are highly dependent on both quantity and quality of the input data used, gathering a statistically significant number of datasets is a prerequisite for the successful application of productivity measurement methodologies. In this paper, we present an approach to automated data extraction developed in cooperation with industry partners. Our concepts are based on the evaluation of a large collection of logfile data generated by a state-of-the-art workflow in the semiconductor industry and on staff feedback. The approach aims at providing an easy-to-use data extraction framework that can be integrated within a current work environment. The experiences gathered in the process of implementing and using our approach result in recommendations for a future unified data format for tool logfiles.
ASJC Scopus subject areas
- Decision Sciences(all)
- Information Systems and Management
- Engineering(all)
- Electrical and Electronic Engineering
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
IEMC-Europe 2008 - 2008 IEEE International Engineering Management Conference, Europe: Managing Engineering, Technology and Innovation for Growth. 2008. 4617971 (IEMC-Europe 2008 - 2008 IEEE International Engineering Management Conference, Europe: Managing Engineering, Technology and Innovation for Growth).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Automatic data extraction
T2 - IEMC-Europe 2008 - 2008 IEEE International Engineering Management Conference, Europe: Managing Engineering, Technology and Innovation for Growth
AU - Zaum, D.
AU - Olbrich, M.
AU - Barke, E.
PY - 2008
Y1 - 2008
N2 - Improving the productivity of any business process initially requires its measurement. Therefore, automated models for comparison, simulation and analysis of products and the appendant workflows are being developed and improved constantly. Since the results delivered by such automatically trained systems are highly dependent on both quantity and quality of the input data used, gathering a statistically significant number of datasets is a prerequisite for the successful application of productivity measurement methodologies. In this paper, we present an approach to automated data extraction developed in cooperation with industry partners. Our concepts are based on the evaluation of a large collection of logfile data generated by a state-of-the-art workflow in the semiconductor industry and on staff feedback. The approach aims at providing an easy-to-use data extraction framework that can be integrated within a current work environment. The experiences gathered in the process of implementing and using our approach result in recommendations for a future unified data format for tool logfiles.
AB - Improving the productivity of any business process initially requires its measurement. Therefore, automated models for comparison, simulation and analysis of products and the appendant workflows are being developed and improved constantly. Since the results delivered by such automatically trained systems are highly dependent on both quantity and quality of the input data used, gathering a statistically significant number of datasets is a prerequisite for the successful application of productivity measurement methodologies. In this paper, we present an approach to automated data extraction developed in cooperation with industry partners. Our concepts are based on the evaluation of a large collection of logfile data generated by a state-of-the-art workflow in the semiconductor industry and on staff feedback. The approach aims at providing an easy-to-use data extraction framework that can be integrated within a current work environment. The experiences gathered in the process of implementing and using our approach result in recommendations for a future unified data format for tool logfiles.
UR - http://www.scopus.com/inward/record.url?scp=53149122106&partnerID=8YFLogxK
U2 - 10.1109/IEMCE.2008.4617971
DO - 10.1109/IEMCE.2008.4617971
M3 - Conference contribution
AN - SCOPUS:53149122106
SN - 9781424422890
T3 - IEMC-Europe 2008 - 2008 IEEE International Engineering Management Conference, Europe: Managing Engineering, Technology and Innovation for Growth
BT - IEMC-Europe 2008 - 2008 IEEE International Engineering Management Conference, Europe
Y2 - 28 June 2008 through 30 June 2008
ER -