Software Engineering for Big Data Systems

被引：13
|
作者：
Gorton, Ian [1 ]
Bener, Ayse Basar [2 ,3 ,4 ]
Mockus, Audris [5 ]
机构：
[1] Northeastern Univ, Comp Sci Masters Programs, Boston, MA 02115 USA
[2] Ryerson Univ, Data Sci Lab, Dept Mech & Ind Engn, Toronto, ON, Canada
[3] Ryerson Univ, Off Provost, Big Data, Toronto, ON, Canada
[4] Ryerson Univ, Toronto, ON, Canada
[5] Univ Tennessee, Dept Elect Engn & Comp Sci, Knoxville, TN USA
来源：
IEEE SOFTWARE | 2016年 / 33卷 / 02期
关键词：
big data; data analytics; online video processing; operational-log analysis; software development; software engineering; strategic prototyping;
D O I：
10.1109/MS.2016.47
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
Software engineering for big data systems is complex and faces challenges including pervasive distribution, write-heavy workloads, variable request loads, computation-intensive analytics, and high availability. The articles in this theme issue examine several facets of this complicated puzzle. The Web extra at https://youtu.be/YKBGf9EOBUo is an audio recording of Davide Falessi speaking with Ayse Basar Bener and Audris Mockus about the authors, articles, and discussions that went into the IEEE Software March/April 2016 theme issue on software engineering for big data systems. © 1984-2012 IEEE.
引用
收藏
页码：32 / 35
页数：4
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