An Efficient Divide and Conquer Approach for Big Data Analytics in Machine to Machine Communication

Mukesh Nair, Prof. Amruta Chadawar, Ashitesh Bhosle

Many Machine-to-Machine communications relies on the physical objects like satellites, sensors etc interconnected with each other, creating mesh of machines producing massive volume of data about large geographical areas. Thus, the Machine-to-Machine is an ideal example of Big Data. On the contrary, the Machine-to-Machine platforms that handle Big Data might perform poorly or not according to the goals of their operator in terms of the cost, database utilization, data quality, processing and computational efficiency, analysis etc. Therefore, to address the aforementioned needs, we propose a new effective, memory and processing efficient system architecture for Big Data in M2M, which, unlike other previous proposals, does not require whole set of data to be processed (including raw data sets), and to be kept in the main memory. Our designed system architecture exploits divide-and-conquer approach and data block-wise vertical representation of the data- base follows a particular petitionary strategy, which formalizes the problem of feature extraction applications. The architecture goes from physical objects to the processing servers, where Big Data set is first transformed into a several data blocks that can be quickly processed, then it classifies and reorganizes these data blocks from the same source. In addition, the data blocks are aggregated in a sequential manner based on a machine ID, and equally partitions the data using fusion algorithm. Finally, the results are stored in a server that helps the users in making decision.