'Large scale data analysis' diaporamas de présentation

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John Kubiatowicz Electrical Engineering and Computer Sciences University of California, Berkeley

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Hadoop & Map Reduce

Hadoop & Map Reduce. Presentation by Yoni Nesher NonSQL database Techforum. Hadoop & Map Reduce. Forum Agenda: Big data problem domain Hadoop ecosystem Hadoop Distributed File System (HDFS) Diving in to MapReduce MapReduce case studies

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Approximate computation and implicit regularization in large-scale data analysis

Approximate computation and implicit regularization in large-scale data analysis. Michael W. Mahoney Stanford University Jan 2013 (For more info, see: http://cs.stanford.edu/people/mmahoney). How do we view BIG data?. Algorithmic & Statistical Perspectives. Lambert (2000).

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Multicore and Cloud Technologies for Data Intensive Applications

Multicore and Cloud Technologies for Data Intensive Applications. Judy Qiu xqiu@indiana.edu www.infomall.org/s a lsa Pervasive Technology Institute Indiana University. Ballantine Hall 006 , Indiana University Bloomington October 23, 2009. Abstract.

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Microsoft Cloud Computing Platform

Microsoft Cloud Computing Platform. Roger Barga Dennis Gannon Wei Lu External Research, MSR Cloud Computing Futures, MSR. Outline. What is Cloud Computing? Data Center Design Issues Cloud Software Infrastructure A Brief Introduction to Azure Computation in the Cloud

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Today ’ s Emerging Platforms, Tomorrow ’ s Cyber Infrastructure

Today ’ s Emerging Platforms, Tomorrow ’ s Cyber Infrastructure. OBAMA ADMINISTRATION UNVEILS $200 Million “ BIG DATA ” INITIATIVE (March 2012)

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Massive Database Analysis on the Cloud with D4M

UNCLASSIFIED // FOR OFFICIAL USE ONLY. Massive Database Analysis on the Cloud with D4M. Jeremy Kepner

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Future of Analysis Environments Personal views

Future of Analysis Environments Personal views. Rene Brun CERN. Type of data ? Any type ? PAW-like ntuple?. No restrictions. Data. Restricted to histogramming & visualisation ?. Analysis. Structure ? What is modularity? Abstract interfaces? Languages? Parallelism?. Coherent

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Distributed Computing

Distributed Computing. Rik Sarkar. Distributed Computing. Old style: Use a computer for computation. Distributed Computing. New style: Use many computers for computation. Examples:. Computing clusters (data analysis and storage, hadoop etc) Networks of sensors Mobile systems.

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Neuroinformatics Research at UO

Neuroinformatics Research at UO. Experimental Methodology and Tool Integration. 16x256 bits per millisec (30MB/m). CT / MRI. segmented tissues. EEG. NetStation. BrainVoyager. processed EEG. mesh generation. source localization constrained to cortical surface. Interpolator 3D. EMSE.

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Potential Projects

CS294 Fall, 2011 Communication-Avoiding Algorithms www.cs.berkeley.edu/~odedsc/CS294. Potential Projects. Jim Demmel. Types of Projects. Read and report on a paper Design, implement and/or test an algorithm Extend the result of the paper you read

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Felix Cuadrado felix.cuadrado@qmul.ac.uk eecs.qmul.ac.uk/~fcuadrado/

Felix Cuadrado felix.cuadrado@qmul.ac.uk http://www.eecs.qmul.ac.uk/~fcuadrado/. Teaching : Big Data Processing (QMUL) Internet Protocols and Applications (QMUL) Cloud Computing (JP- IoT ). Chinese Cloud Ecosystem. Benchmarking of cloud applications Network and server measurements

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Memcached Integration with Twister

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