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In the real time scenario, the volume of data used linearly increases with time. Social networking sites like Facebook, Twitter discovered the growth of data which will be uncontrollable in the future. In order to manage the huge volume of data, the proposed method will process the data in parallel as small chunks in distributed clusters and aggregate all the data across clusters to obtain the final processed data. In Hadoop framework, MapReduce is used to perform the task of filtering, aggregation and to maintain the efficient storage structure. The data are preferably refined using collaborative filtering, under the prediction mechanism of particular data needed by the user. The proposed method is enhanced by using the techniques such as sentiment analysis through natural language processing for parsing the data into tokens and emoticon based clustering. The process of data clustering is based on user emotions to get the data needed by a specific user. The results show that the proposed approach significantly increases the performance of complexity analysis.

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Summary A model predictive current control (MPCC)‐based single‐phase shunt active power filter (SAPF) is proposed in this manuscript to improve power quality by harmonics and reactive power compensation. The DC link capacitor voltage regulation‐based PI control algorithm is used for the computation of SAPF reference current signal. The current commands are tracked using the MPCC. In this manuscript, the control technique effectiveness is demonstrated through MATLAB simulation and experimentation with Cyclone‐IV EP4CE30F484 FPGA board under dynamic operating conditions. The obtained results from a simulations and hardware prototype shows the proposed algorithm shows perfect reference current tracking at every sampling instant and excellent dynamic performance. Subsequently, the main advantages of this proposed controller are absence of internal current control loops and modulation stage.

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