Title	Big Data and Small Devices
Creator	Morik, Katharina
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2015-07-25T11:44
Description	Big data are produced by various sources. Most often, they are distributedly stored at computing farms or clouds. Analytics on the Hadoop Distributed File System (HDFS) then follows the MapReduce programming model (batch layer). It is complemented by the speed layer, which aggregates and integrates incoming data streams in real time. When considering big data and small devices, obviously, we imagine the small devices being hosts of the speed layer, only. Analytics on the small devices is restricted by memory and computation resources. The interplay of streaming and batch analytics offers a multitude of configurations. The collaborative research center SFB 876 investigates data analytics for and on small devices regarding runtime, memory and energy consumption. In this talk, we investigate graphical models, which generate the probabilities for connected (sensor) nodes. Resource-restricted methods deliver insights fast enough for a more interactive analysis.
Extent	46 minutes
Subject	Mathematics; Computer science; Biology and other natural sciences
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: TU Dortmund University
Series	BIRS Workshop Lecture Videos (Banff, Alta)
Date Available	2016-03-08
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0227974
URI	http://hdl.handle.net/2429/57084
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Faculty
Rights URI	http://creativecommons.org/licenses/by-nc-nd/4.0/
Aggregated Source Repository	DSpace

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Big Data and Small Devices Morik, Katharina

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