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|Author(s):||David J. Lechevalier; Anantha Narayanan Narayanan; Sudarsan Rachuri;|
|Title:||Towards a Domain-Specific Framework for Predictive Analytics in Manufacturing|
|Published:||October 03, 2014|
|Abstract:||Data analytics is proving to be very useful for achieving productivity gains in manufacturing. Predictive analytics (using advanced machine learning) is particularly valuable in manufacturing, as it leads to production improvement with respect to the cost, quantity, quality and sustainability of manufactured products by anticipating changes to the manufacturing system states. Many small and medium manufacturers do not have the infrastructure, technical capability or financial means to take advantage of predictive analytics. A domain-specific language and framework for performing predictive analytics for manufacturing and production frameworks can counter this deficiency. In this paper, we survey some of the applications of predictive analytics in manufacturing and we discuss the challenges that need to be addressed. Then, we propose a core set of abstractions and a domain-specific framework for applying predictive analytics on manufacturing applications. Such a framework will allow manufacturers to take advantage of predictive analytics to improve their production.|
|Proceedings:||ACM/IEEE 17th International Conference on Model Driven Engineering Languages and Systems|
|Dates:||September 28-October 3, 2014|
|Keywords:||domain specific modeling, data analytic, machine learning, manufacturing|
|Research Areas:||Data and Informatics, Model-based manufacturing|
|PDF version:||Click here to retrieve PDF version of paper (668KB)|