PPT-Large-scale Data Processing Challenges

David Wallom Overview The problem Other communities The pace of technological change Using the data The problem New telescopes generate vast amounts of data Particularly but not limited to surveys SDSS PANSTARRS LOFAR SKA. By. Chi . Bemieh. . Fule. August 6, 2013. THESIS PRESENTATION . Outline. . of. . today’s. presentation. Justification of the study. Problem . statement. Hypotheses. Conceptual. . framework. Research . Large-scale Single-pass k-Means . Clustering. Large-scale . k. -Means Clustering. Goals. Cluster very large data sets. Facilitate large nearest neighbor search. Allow very large number of clusters. Achieve good quality. Kamini. Yadav. Dr. Russ . Congalton. Current Process Flow Chart. Testing Protocol on Mali data. Evaluate Mali data collected in August 2015 by . Murali. , according to the flowchart made by Justin. Large Scale. 600.466. Social Media?. Properties of Social Media : Scale. Twitter (Chirp 2010). More than 100M user accounts. more than 600M search queries a day. 55M tweets a day. Facebook. More than 400M active users. Abstract. Cloud computing economically enables customers with limited computational resources to outsource large-scale computations to the cloud. . However, how to protect customers’ confidential data involved in the computations then becomes a major security concern. In this paper, we present a secure outsourcing mechanism for solving large-scale systems of linear equations (LE) in cloud.. Frank . Würthwein. moderator . 16 October 2015. What is the PRP? . Ecosystem and networking. PRP is the answer to all research IT problems?. PRP is the answer to all networking problems?. PRP is 3 FTEs to change the world. SpatialHadoop. Type: Research Paper (Experimental evaluation). Authors: Ahmed . Eldawy. , . Louai. . Alarabi. , Mohamed F. . Mokbel. Presented by: Siddhant Kulkarni. Term: Fall 2015. Motivation. and Projects . Big Data . Applications and Generalizing . their Structure. I590 Data Science Curriculum. August 16 2014. Geoffrey Fox . gcf@indiana.edu. . . http://www.infomall.org. School of Informatics and Computing. Operator State Management. with . Raul Castro Fernandez*. Matteo. . Migliavacca. +. and Peter . Pietzuch. *. *. Imperial College London, . +. Kent . Univerisity. Big data … . … in numbers: . 2.5 billions on gigabytes of data every day . : From the . Very Large . to the . Very Small. Saurabh Bagchi. School of Electrical and Computer Engineering. Department of Computer Science . Purdue University. Presentation available at: . engineering.purdue.edu/dcsl. Feng Zhang †⋄, . Jidong. . Zhai. ⋄, . Xipeng. Shen #, . Onur. . Mutlu. ⋆, . Wenguang. Chen ⋄. †Renmin University of China. ⋄Tsinghua University. #North Carolina State University. 7/12/2014. CSE651C, B.Ramamurthy. 1. Big-Data computing. 7/12/2014. 2. What is it?. Volume, velocity, variety, veracity (uncertainty) (Gartner, IBM). How is it addressed? . Why now? . What do you expect to extract by processing this large data?. Ashvin Goel. Electrical and Computer Engineering. University of Toronto. ECE 1724, Winter 2020. Topics. Overview of the course. Class format. Introduction to the course. My Research Background. S. ystems software. . technological. changes. Networks. Parallelism. . (Cluster . computing. & . Multicore. ). Storage . systems. Processor . technology. New . applications. Web . apps. Streaming . data . apps. Business .