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Industries are generally data rich but information poor environments. Massive data generated in industrial operations is traditionally neglected (or simply took aside) mainly due to systems design restrictions, to the lack of adequate processing power of typically installed computing infrastructure and to a sector culture notably focused on collecting, selecting, storing and preserving historical series in on-demand access repositories. This huge amount of unprocessed data resting in these repositories is a latent source of information that could be used to improve industrial processes. This work then proposes an approach in which a proper computing power processing engine is plugged-in to current industrial information infrastructure to provide it with the ability of handling massive industrial data. Testing on real-world industrial data volumes of 5GB, 50GB and 100GB attested the effectiveness and potential of the proposed approach in dealing with Industrial Big Data.

  • 1 Universidade Federal do Rio Grande do Norte
Eixo Temático
  • Aplicações na Indústria
Industrial Big Data
Information Infrastructure
Parallel processing