Big Data Processing and Analytics Platform Architecture for Process Industry Factories
AbstractThis paper describes the architecture of a cross-sectorial Big Data platform for the process industry domain. The main objective was to design a scalable analytical platform that will support the collection, storage and processing of data from multiple industry domains. Such a platform should be able to connect to the existing environment in the plant and use the data gathered to build predictive functions to optimize the production processes. The analytical platform will contain a development environment with which to build these functions, and a simulation environment to evaluate the models. The platform will be shared among multiple sites from different industry sectors. Cross-sectorial sharing will enable the transfer of knowledge across different domains. During the development, we adopted a user-centered approach to gather requirements from different stakeholders which were used to design architectural models from different viewpoints, from contextual to deployment. The deployed architecture was tested in two process industry domains, one from the aluminium production and the other from the plastic molding industry. View Full-Text
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Sarnovsky, M.; Bednar, P.; Smatana, M. Big Data Processing and Analytics Platform Architecture for Process Industry Factories. Big Data Cogn. Comput. 2018, 2, 3.
Sarnovsky M, Bednar P, Smatana M. Big Data Processing and Analytics Platform Architecture for Process Industry Factories. Big Data and Cognitive Computing. 2018; 2(1):3.Chicago/Turabian Style
Sarnovsky, Martin; Bednar, Peter; Smatana, Miroslav. 2018. "Big Data Processing and Analytics Platform Architecture for Process Industry Factories." Big Data Cogn. Comput. 2, no. 1: 3.
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