[논문]Toward modeling and optimization of features selection in Big Data based social Internet of Things

Ahmad, Awais; Khan, Murad; Paul, Anand; Din, Sadia; Rathore, M. Mazhar; Jeon, Gwanggil; Choi, Gyu Sang

doi:10.1016/j.future.2017.09.028

Toward modeling and optimization of features selection in Big Data based social Internet of Things

Future generation computer systems : FGCS, v.82, 2018년, pp.715 - 726

Ahmad, Awais (Department of Information and Communication Engineering, Yeungnam University) , Khan, Murad (Department of Computer Science, Sarhad University of Science and Information Technology) , Paul, Anand (School of Computer Science and Engineering, Kyungpook National University) , Din, Sadia (School of Computer Science and Engineering, Kyungpook National University) , Rathore, M. Mazhar (School of Computer Science and Engineering, Kyungpook National University) , Jeon, Gwanggil (Department of Embedded Systems Engineering, Incheon National University) , Choi, Gyu Sang (Department of Information and Communication Engineering, Yeungnam University)

Abstract ▼ AI-Helper

Abstract The growing gap between users and the Big Data analytics requires innovative tools that address the challenges faced by big data volume, variety, and velocity. Therefore, it becomes computationally inefficient to analyze and select features from such massive volume of data. Moreover, advancements in the field of Big Data application and data science poses additional challenges, where a selection of appropriate features and High-Performance Computing (HPC) solution has become a key issue and has attracted attention in recent years. Therefore, keeping in view the needs above, there is a requirement for a system that can efficiently select features and analyze a stream of Big Data within their requirements. Hence, this paper presents a system architecture that selects features by using Artificial Bee Colony (ABC). Moreover, a Kalman filter is used in Hadoop ecosystem that is used for removal of noise. Furthermore, traditional MapReduce with ABC is used that enhance the processing efficiency. Moreover, a complete four-tier architecture is also proposed that efficiently aggregate the data, eliminate unnecessary data, and analyze the data by the proposed Hadoop-based ABC algorithm. To check the efficiency of the proposed algorithms exploited in the proposed system architecture, we have implemented our proposed system using Hadoop and MapReduce with the ABC algorithm. ABC algorithm is used to select features, whereas, MapReduce is supported by a parallel algorithm that efficiently processes a huge volume of data sets. The system is implemented using MapReduce tool at the top of the Hadoop parallel nodes with near real-time. Moreover, the proposed system is compared with Swarm approaches and is evaluated regarding efficiency, accuracy and throughput by using ten different data sets. The results show that the proposed system is more scalable and efficient in selecting features. Highlights Presents an architecture for Social Internet of Things. Modeling and optimization of Big Data. System architecture based on ABC. MapReduce with ABC to enhance system efficiency.

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