An Efficient Multidimensional Big Data Fusion Approach in Machine-to-Machine Communication

摘要: Machine-to-Machine communication (M2M) is nowadays increasingly becoming a world-wide network of interconnected devices uniquely addressable, via standard protocols. The prevalence M2M bound to generate massive volume heterogeneous, multisource, dynamic, and sparse data, which leads system towards major computational challenges, such as, analysis, aggregation, storage. Moreover, critical problem arises extract the useful information in an efficient manner from data. Hence, govern adequate quality diverse capacious data needs be aggregated fused. Therefore, it imperative enhance efficiency for fusing analyzing address these issues, this article proposes efficient, multidimensional, big analytical architecture based on fusion model. basic concept implicates division magnitudes (attributes), i.e., datasets with complex can altered into smaller subsets using five levels model that easily processed by Hadoop Processing Server, resulting formalizing feature extraction applications earth observatory system, social networking, or networking applications. four-layered also proposed fulfills requirements architecture. feasibility algorithms used are implemented single-node setup UBUNTU 14.04 LTS core i5 machine 3.2GHz processor 4GB memory. results show efficiently extracts various features (such as land sea) satellite