GPU Accelerated MapReduce-Based Distributed Framework for Knowledge Extraction from Large Uncertain Data

Knowledge extraction from large, uncertain datasets has become a new challenge due to the rapid growth of data. In the present world, where petabytes of data are being generated within a fraction of a second, certain mechanisms are needed to analyse it and extract useful features from it. In this paper, we propose an efficient distributed framework under the GPU-based MapReduce paradigm to process the large, uncertain data and extract the interim knowledge or structural relationships present within the data itself. This framework involves two phases. The first phase reduces the dimension of large data by partitioning the data based on entropy and coverage value. The second phase extracts the important features from the reduced data using a positive region value based on Rough Set Theory. Our approach efficiently optimises the feature set that exhibits the essential information from the large dataset. The proposed framework processes data in parallel, which reduces the processing time and gives more speed and efficiency. The effectiveness of our proposed method compared to previous works has been shown by comprehensive experimental analysis on large datasets.