The wide adoption of the Wireless Senor Networks (WSNs) applications around the world has increased the amount of the sensor data which contribute to the complexity of Big Data. This has emerged the need to the use of in-network data processing techniques which are very crucial for the success of the big data framework.
Big Data is a massive set of data that is so complex to be managed by traditional applications. Nowadays, it includes huge, complex, and abundant structured, semi-structure, and unstructured data as well as hidden data that are generated and gathered from several fields and resources. The challenges for managing Big Data include extracting, analyzing, visualizing, sharing, storage, transferring and searching such data. Currently, the traditional data processing tools and its applications are not capable of managing such revolutionized data. Therefore, there is a critical need to develop effective and efficient Big Data Mining techniques. This, in turn, has opened opportunities for research frontiers by using the exploiting artificial intelligence techniques for Big Data management. This study investigates the most effective Big Data Mining techniques and their rationale applications in various social, medical and scientific fields.
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