Paper Info
Title
A Framework for Cloud-Based Large-Scale Data Analytics and Visualization: Case Study on Multiscale Climate Data
Abstract
In this paper, we present a cloud framework to provide cloud clustering, workflow scheduling and management, fault tolerance and distributed data storage, data analytics and visualisation services. Using a practical case study, we show that in the process of analyzing multiscale climate data, typical problems plaguing data analysts are faced. These include large datasets and limited computational resources, data complexity and limited knowledge, and varying data structures/formats and the need to integrate different tools. The implementation of our framework to climate studies was a success. This can be seen in its ability to perform spatio-temporal data analysis and visualization of a large multi-dimensional climate dataset with reduced processing time. The framework demonstrates great flexibility and simplicity for end users intending to perform data analysis by aiding the integration of data and tools and enabling interactive visualization on-the-fly. This is coupled with effective utilization of computational resources and data storage systems.
Year
DOI
Venue
2011
10.1109/CloudCom.2011.95
CloudCom
Keywords
Field
DocType
data analysis,varying data structure,multiscale climate data,data complexity,multiscale climate,cloud-based large-scale data analytics,case study,data analyst,spatio-temporal data analysis,data analytics,data storage system,data storage,climate study,data visualisation,cloud computing,fault tolerant,engines,data visualization,meteorology,interactive visualization,distributed data storage,data integration,data mining,trajectory,geographic information systems,computational complexity,data structure,fault tolerance
Data integration,Data science,Data mining,Data structure,Data visualization,Data analysis,Computer science,Visualization,Interactive visualization,Cluster analysis,Cloud computing
Conference
Citations 
PageRank 
References 
14
0.95
1
Authors
7
Name
Order
Citations
PageRank
Sifei Lu1344.44
Reuben Mingguang Li2221.57
William-Chandra Tjhi315610.09
Kee Khoon Lee4262.34
Long Wang5142.30
Xiaorong Li611310.80
Di Ma79210.75