Data Mining Applications in Steel Industry

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Author(s): Joaquín Ordieres-Meré (University of La Rioja, Spain), Manuel Castejón-Limas (University of León, Spain)and Ana González-Marcos (University of León, Spain)
Copyright: 2009
Pages: 6
Source title: Encyclopedia of Data Warehousing and Mining, Second Edition
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-60566-010-3.ch063

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Abstract

The industrial plants, beyond subsisting, pursue to be leaders in increasingly competitive and dynamic markets. In this environment, quality management and technological innovation is less a choice than a must. Quality principles, such as those comprised in ISO 9000 standards, recommend companies to make their decisions with a based on facts approach; a policy much easily followed thanks to the all-pervasive introduction of computers and databases. With a view to improving the quality of their products, factory owners are becoming more and more interested in exploiting the benefits gained from better understanding their productive processes. Modern industries routinely measure the key variables that describe their productive processes while these are in operation, storing this raw information in databases for later analysis. Unfortunately, the distillation of useful information might prove problematic as the amount of stored data increases. Eventually, the use of specific tools capable of handling massive data sets becomes mandatory. These tools come from what it is known as ‘data mining’, a discipline that plays a remarkable role at processing and analyzing massive databases such as those found in the industry. One of the most interesting applications of data mining in the industrial field is system modeling. The fact that most frequently the relationships amongst process variables are nonlinear and the consequent difficulty to obtain explicit models to describe their behavior leads to data-based modeling as an improvement over simplified linear models.

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