Abstract
The main objective of this chapter is to highlight some new product-driven system issues. Several effective options have been proposed that allow products or objects to react to environmental modifications, especially in the manufacturing and logistics contexts considered in the current study. At present, bio-inspired approaches are particularly promising. These new methods allow products to respond to the information that they collect. This is why techniques that facilitate the exploitation and organization of data are necessary. The main objective of this chapter is addressed in the second section, where we highlight why learning machines may be viewed as a new way of transforming data into useful knowledge.
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Thomas, P., Thomas, A. (2013). An Approach to Data Mining for Product-driven Systems. In: Borangiu, T., Thomas, A., Trentesaux, D. (eds) Service Orientation in Holonic and Multi Agent Manufacturing and Robotics. Studies in Computational Intelligence, vol 472. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35852-4_12
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DOI: https://doi.org/10.1007/978-3-642-35852-4_12
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