The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.06 - Nov.-Dec. (2013 vol.30)
pp: 33-39
Panos Louridas , Athens University of Economics and Business
Christof Ebert , Vector Consulting Services
ABSTRACT
Embedded analytics and statistics for big data have emerged as an important topic across industries. As the volumes of data have increased, software engineers are called to support data analysis and applying some kind of statistics to them. This article provides an overview of tools and libraries for embedded data analytics and statistics, both stand-alone software packages and programming languages with statistical capabilities.
INDEX TERMS
Big Data, Programming, Embedded systems, Data handling, Linux, Information management,statistics, software technology, embedded analytics, big data
CITATION
Panos Louridas, Christof Ebert, "Embedded Analytics and Statistics for Big Data", IEEE Software, vol.30, no. 6, pp. 33-39, Nov.-Dec. 2013, doi:10.1109/MS.2013.125
REFERENCES
1. C. Ebert and R. Dumke, Software Measurement, Springer, 2007.
2. K. Michael and K.W. Miller eds., Computer, vol. 46, no. 6, 2013.
3. T. Menzies, and T. Zimmermann eds., IEEE Software, vol. 30, no. 4, 2013.
4. W. McKinney, Python for Data Analysis: Agile Tools for Real World Data, O'Reilly Media, 2012.
5. J. Adler, R in a Nutshell, 2nd ed., O'Reilly Media, 2012.
6. M. Bostock, V. Ogievetsky, and J. Heer, “D3: Data-Driven Documents,” IEEE Trans. Visualization and Computer Graphics, vol. 17, no. 12, 2011, pp. 2301–2309.
7. R.A. Muenchen, “The Popularity of Data Analysis Software,” 2010; http://r4stats.com/articlespopularity.
59 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool