Performance Enhancement of Distributed Clustering for Big Data Analytics

被引：0
|
作者：
Mohamed, Omar Hesham [1 ]
Shehab, Mohamed Elemam [2 ]
El Fakharany, Essam [3 ]
机构：
[1] Arab Acad Sci Technol & Maritime Transport, Informat Syst Dept, Cairo, Egypt
[2] Arab Acad Sci Technol & Maritime Transport, Cairo, Egypt
[3] Arab Acad Sci Technol & Maritime Transport, Coll Comp & Informat Technol, Cairo, Egypt
来源：
INTERNATIONAL CONFERENCE ON ADVANCED MACHINE LEARNING TECHNOLOGIES AND APPLICATIONS (AMLTA2018) | 2018年 / 723卷
关键词：
Big Data; Apache Spark; Machine learning algorithms; K-Means algorithm; In-memory computation; Big data analytic;
D O I：
10.1007/978-3-319-74690-6_41
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Big Data analytics are recently coming up as prominent research area in the field of data science. Apache Spark is an open source distributed data processing platform that uses distributed memory abstraction to process large volume of streaming data efficiently. Performance improvement of analytic computational model of streaming big data is important to meet the requirements of many real-time data analysis. Researchers focus on Analytic algorithm improvement to reduce analysis time. This paper presents performance enhancement of in-memory computational model by selecting the most important attributes after caching data to Apache spark. Performance analysis of distributed K-Means clustering algorithm based on in-memory computational model has been conducted. The results show improvement in the performance of the model.
引用
收藏
页码：415 / 425
页数：11
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