Big Data reduction is a main point of interest across a wide variety of fields. This domain was further investigated when the difficulty in quickly acquiring the most useful information from the huge amount of data at hand was encountered. To achieve the task of data reduction, specifically feature selection, several state-of-the-art methods were proposed. However, most of them require additional information about the given data for thresholding, noise levels to be specified or they even need a feature ranking procedure. Thus, it seems necessary to think about a more adequate feature selection technique which can extract features using information contained within the dataset alone. Rough Set Theory (RST) can be used as such a technique to discover data dependencies and to reduce the number of features contained in a dataset using the data alone, requiring no additional information. However, despite being a powerful feature selection technique, RST is computationally expensive and only practical for small datasets. Therefore, in this paper, we present a novel efficient distributed Rough Set Theory based algorithm for large-scale data pre-processing under the Spark framework. Our experimental results show the efficient applicability of our RST solution to Big Data without any significant information loss.

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