Paper Info
Title
Toward modeling and optimization of features selection in Big Data based social Internet of Things.
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.
Year
DOI
Venue
2018
10.1016/j.future.2017.09.028
Future Generation Computer Systems
Keywords
Field
DocType
SIoT,Big Data,ABC algorithm,Feature selection
Data mining,Data set,Swarm behaviour,Parallel algorithm,Computer science,Kalman filter,Systems architecture,Throughput,Big data,Scalability,Distributed computing
Journal
Volume
ISSN
Citations 
82
0167-739X
6
PageRank 
References 
Authors
0.41
30
7
Name
Order
Citations
PageRank
Awais Ahmad137945.85
Murad Khan215022.14
Anand Paul352746.32
Sadia Din48719.55
muhammad mazhar ullah rathore530121.15
Gwanggil Jeon6596117.99
Gyu Sang Choi712120.20