A Big Data Framework for Mining Sensor Data Using Hadoop
Engy A. EL-SHAFEYI*, Ali I. EL-DESOUKY
Computers and Systems Department, Faculty of Engineering,
Mansoura University, Egypt
(*Corresponding author) e-mail: firstname.lastname@example.org.
ABSTRACT: The data gathered from IOTs is considered of high business value. The IOTs devices sense the natural conditions using sensor network comprised of sensor nodes. Mining of big sensor data for useful knowledge extraction is a very challenging task. Frequent itemsets is one of the most effective mining techniques that find important itemsets from big sensor data. In this paper, a MapReduce Frequent Nodesets-based Boundary POC tree (MR-FNBP) framework is proposed for mining Frequent Nodesets for big sensor data. The MapReduce framework is used to implement MR-FNBP to enhance its performance in highly distributed environments. Additionally, the proposed Boundary (FNBP) creates a Boundary as an early stage to exclude the infrequent itemsets, and this may reduce the overall memory and time usage. Moreover, a number of experiments were performed to evaluate the performance of MR-FNBP framework. The results show high scalability rate and a less time consuming process for MR-FNBP framework over different recent systems.
KEYWORDS: Big data, Internet of Things, MapReduce, Wireless Sensor Networks, Mining Frequent Nodesets.
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CITE THIS PAPER AS:
Engy A. EL-SHAFEYI*, Ali I. EL-DESOUKY, A Big Data Framework for Mining Sensor Data Using Hadoop, Studies in Informatics and Control, ISSN 1220-1766, vol. 26(3), pp. 365-376, 2017.