PPT-CSCI5570 Large Scale Data Processing Systems

Tatiana Jin and James Cheng Oct 24 2017 1 Paxos A Consensus Algorithm For FaultTolerant Distributed Systems Oct 24 2017 2 Introduction Consensus Requirements Asynchronicity amp Unreliability. 3.3.2. This presentation covers:. Batch, interactive and real-time processing systems in terms of processing, response time and user interface requirements.. Introduction. Data is pretty useless unless it is processed in some way.. 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. 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, PAN-STARRS, LOFAR, SKA…). 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. 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. Wenting . Wang. Le Xu. Indranil Gupta. Department of Computer Science, University of Illinois, Urbana Champaign . 1. Scale up VS. Scale out. A dilemma for cloud application users: scale up or scale out? . Barzan. . Mozafari. (presented by Ivo D. Dinov). University of Michigan, Ann Arbor. Research Goals. Using statistics to build better data-intensive systems. Faster. How to query petabytes of data in seconds?. 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 . 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.