Semi-automatic Maintenance of Regression Models: an Application in the Steel Industry. Juutilainen I Tuovinen L, L., P., K., H., R., J. Journal of Computing and Information Technology, 19(3):71-82, 2011. abstract bibtex Software applications used in the controlling and planning of
production processes commonly make use of predictive statistical
models. Changes in the process involve a more or less regular need
for updating the prediction models on which the operational software applications
are based. The objective of this article is
to provide information which helps to design semi-automatic systems for the maintenance of statistical prediction models and
to describe a proof-of-concept implementation in an industrial application.
The system developed processes the production data and provides an easy-to-use interface to construct updated models and introduce them into a software application. The article presents the architecture of the maintenance system, with a description of the algorithms that cause the system's functionality. The system developed was implemented for keeping up-to-date prediction models which are in everyday use in a steel
plate mill in the planning of the mechanical properties of steel products. The conclusion of the results is that the semi-automatic approach proposed is competitive with fully automatic and manual approaches. The benefits include good prediction accuracy and decreased workload of the deployment of updated model versions.
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title = {Semi-automatic Maintenance of Regression Models: an Application in the Steel Industry},
type = {article},
year = {2011},
pages = {71-82},
volume = {19},
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last_modified = {2019-11-19T13:48:32.293Z},
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citation_key = {isg:1605},
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abstract = {Software applications used in the controlling and planning of
production processes commonly make use of predictive statistical
models. Changes in the process involve a more or less regular need
for updating the prediction models on which the operational software applications
are based. The objective of this article is
to provide information which helps to design semi-automatic systems for the maintenance of statistical prediction models and
to describe a proof-of-concept implementation in an industrial application.
The system developed processes the production data and provides an easy-to-use interface to construct updated models and introduce them into a software application. The article presents the architecture of the maintenance system, with a description of the algorithms that cause the system's functionality. The system developed was implemented for keeping up-to-date prediction models which are in everyday use in a steel
plate mill in the planning of the mechanical properties of steel products. The conclusion of the results is that the semi-automatic approach proposed is competitive with fully automatic and manual approaches. The benefits include good prediction accuracy and decreased workload of the deployment of updated model versions.},
bibtype = {article},
author = {Juutilainen I Tuovinen L, Laurinen P Koskimäki H Röning J},
journal = {Journal of Computing and Information Technology},
number = {3}
}
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