Orchestrating BigData Analysis Workflows

被引:28
|
作者
Ranjan, Rajiv [1 ]
Garg, Saurabh [2 ]
Khoskbar, Ali Reza [3 ]
Solaiman, Ellis [1 ]
James, Philip [1 ]
Georgakopoulos, Dimitrios [4 ]
机构
[1] Newcastle Univ, Newcastle Upon Tyne, Tyne & Wear, England
[2] Univ Tasmania, Hobart, Tas, Australia
[3] Australian Natl Univ, Canberra, ACT, Australia
[4] Swinburne Univ Technol, Hawthorn, Vic, Australia
来源
IEEE CLOUD COMPUTING | 2017年 / 4卷 / 03期
基金
英国工程与自然科学研究理事会;
关键词
BigData; cloud computing; cloud tidbits;
D O I
10.1109/MCC.2017.55
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Data analytics has become not only an essential part of day-to-day decision making, but also reinforces long-term strategic decisions. Whether it is real-time fraud detection, resource management, tracking and prevention of disease outbreak, natural disaster management or intelligent traffic management, the extraction and exploitation of insightful information from unparalleled quantities of data (Big-Data) is now a fundamental part of all decision making processes. Success in making smart decisions by analyzing BigData is possible due to the availability of improved analytical capabilities, increased access to different data sources, and cheaper and improved computing power in the form of cloud computing. However, BigData analysis is far more complicated than the perception created by the recent publicity. For example, one of the myths is that BigData analysis is driven purely by the innovation of new data mining and machine learning algorithms.
引用
收藏
页码:20 / 28
页数:9
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