ICALEPCS2015 - List of Authors (Willingham, D.S.)

Paper	Title	Page
WED3O05	Big Data Analysis and Analytics with MATLAB	656
	D.S. Willingham ASCo, Clayton, Victoria, Australia
	Overview using Data Analytics to turn large volumes of complex data into actionable information can help you improve design and decision-making processes. In today's world, there is an abundance of data being generated from many different sources. However, developing effective analytics and integrating them into existing systems can be challenging. Big data represents an opportunity for analysts and data scientists to gain greater insight and to make more informed decisions, but it also presents a number of challenges. Big data sets may not fit into available memory, may take too long to process, or may stream too quickly to store. Standard algorithms are usually not designed to process big data sets in reasonable amounts of time or memory. There is no single approach to big data. Therefore, MATLAB provides a number of tools to tackle these challenges. In this paper 2 case studies will be presented: 1. Manipulating and doing computations on big datasets on light weight machines; 2. Visualising big, multi-dimensional datasets Developing Predictive Models High performance computing with clusters and Cloud Integration with Databases, HADOOP and Big Data Environments.
	Slides WED3O05 [10.989 MB]
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Paper

Title

Page

Big Data Analysis and Analytics with MATLAB

656

D.S. Willingham
ASCo, Clayton, Victoria, Australia

Overview using Data Analytics to turn large volumes of complex data into actionable information can help you improve design and decision-making processes. In today's world, there is an abundance of data being generated from many different sources. However, developing effective analytics and integrating them into existing systems can be challenging. Big data represents an opportunity for analysts and data scientists to gain greater insight and to make more informed decisions, but it also presents a number of challenges. Big data sets may not fit into available memory, may take too long to process, or may stream too quickly to store. Standard algorithms are usually not designed to process big data sets in reasonable amounts of time or memory. There is no single approach to big data. Therefore, MATLAB provides a number of tools to tackle these challenges. In this paper 2 case studies will be presented: 1. Manipulating and doing computations on big datasets on light weight machines; 2. Visualising big, multi-dimensional datasets Developing Predictive Models High performance computing with clusters and Cloud Integration with Databases, HADOOP and Big Data Environments.

Slides WED3O05 [10.989 MB]

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)