Title	Recent Software Development for Big Data Analysis
Creator	Yan, Jun
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2014-02-11
Description	A Partial Review of Software for Big Data Statistics Big data brings challenges to even simple statistical analysis because of the barriers in computer memory and computing time. The computer memory barrier is usually handled by a database connection that extracts data in chunks for processing. The computing time barrier is handled by parallel computing, often accelerated by graphical processing units. In this partial review, we summarize the open source R packages that break the computer memory limit such as biglm and bigmemory, as well as the academic version of the commercial Revolution R, and R packages that support parallel computing. Products from commercial software will also be sketched for completeness. Joint work with Ming-Hui Chen, Elizabeth Schifano, Chun Wang, and Jing Wu of University of Connecticut.
Extent	98 minutes
Subject	Mathematics; Statistics; Biology and other natural sciences; Applied statistics
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: University of Connecticut
Series	BIRS Workshop Lecture Videos (Banff, Alta)
Date Available	2014-08-07
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivs 2.5 Canada
DOI	10.14288/1.0043885
URI	http://hdl.handle.net/2429/49838
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Faculty
Rights URI	http://creativecommons.org/licenses/by-nc-nd/2.5/ca/
Aggregated Source Repository	DSpace

Open Collections

BIRS Workshop Lecture Videos

Recent Software Development for Big Data Analysis Yan, Jun

Description

Item Metadata

Item Media

Item Citations and Data

Rights