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Modern approaches to preprocessing industrial data

In the context of the current technological progress, big data arises as a compelling research topic. This paper presents non-traditional analysis strategies like exploiting data semantics (cycle identification) as well as traditional ones (signal interpolation and correlation) for industrial data within a Big Data paradigm. A general approach of preprocessing operations for exploring and extracting valuable knowledge from a large set of industrial data is defined. The identified strategies are tested and validated on a real industrial data set characterized by a multitude of complexities. The methodology involves both statistic and semantic processing steps including building various data visualization mechanisms and correlating the signals. Each of the presented approaches handles complexity in the real data.