A parallel multi-objective swarm intelligence framework for Big Data analysis
| Autor: | Amr Mohamed AbdelAziz, Kareem Kamal A. Ghany, Adel Abu El-Magd Sewisy, Taysir Hassan A. Soliman |
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| Rok vydání: | 2020 |
| Předmět: |
education.field_of_study
business.industry Computer science Computer Networks and Communications Data management Big data Population Particle swarm optimization Data science Swarm intelligence Field (computer science) Industrial and Manufacturing Engineering Computer Science Applications Spark (mathematics) Scalability Electrical and Electronic Engineering business education Software Information Systems |
| Zdroj: | International Journal of Computer Applications in Technology. 63:200 |
| ISSN: | 1741-5047 0952-8091 |
| DOI: | 10.1504/ijcat.2020.109342 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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