A parallel multi-objective swarm intelligence framework for Big Data analysis. (25th August 2020)

• Record Type: Journal Article
• Authors: AbdelAziz, Amr Mohamed; Ghany, Kareem Kamal A.; Soliman, Taysir Hassan A.; Sewisy, Adel Abu El-Magd
• Is Part Of: International journal of computer applications technology. Volume 63:Number 3(2020)
• Page Start: 200
• Page End: 212
• Publication Date: 2020-08-25
• Subjects: Big Data -- Big Data analysis -- data mining -- particle swarm optimisation -- multi-objective optimisation -- MapReduce -- Spark; Technology -- Data processing -- Periodicals; 620.00285
• Languages: English
• ISSNs: 0952-8091

Abstract

Nowadays, data are generated from smart devices in huge volumes, different formats, and high pace, which comply with Big Data characteristics. Big Data led to the emergence of new technologies, such as Hadoop and Spark to provide both data management and analysis. Analysing Big Data is a time-consuming process. Particle swarm and ant colony optimisation are population-based meta-heuristic methods. They have been combined with data mining techniques to solve MultiObjective Problems (MOPs) of small and medium sized data, presenting good performance. However, when applying these methods to solve MOPs in Big data, an efficient scalable framework will be required. In this paper, we summarise new technologies proposed to manage and analyse Big Data. We present how meta-heuristics can be adapted with Big Data technologies. We characterise problems arose when analysing MO Big Data problems, in addition to proposed methods to overcome these problems, giving examples in Bioinformatics field.