PPT-An Analysis of Bulk Data Movement Patterns in Large-scale S

W Wu P DeMar A Bobyshev Fermilab CHEP 2010 TAIPEI TAIWAN wenjifnalgov demarfnalgov bobyshevfnalgov Topics Background Problems F ermilab Flow Data Collection amp Analysis System. to Spatial Data Mining. Spatial data mining. is the process of discovering interesting, useful, non-trivial patterns from large . spatial. datasets. Reading Material: . http://en.wikipedia.org/wiki/Spatial_analysis. 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. (. Sociomaterial. ) Inquiry. An approach for analyzing patterns of association . in routines. AoM. PDW 8/1/2014. Kalle Lyytinen. Iris S. Wolstein Professor. E-mail: . kalle@case.edu. . Case Western Reserve University. Jaime . Teevan. , Microsoft . Reseach. UNC 2015. David Foster Wallace. Mark Twain. Cowards die many times . before their deaths. . Annotated by Nelson Mandela. I . have discovered a truly marvelous proof . Jaime . Teevan. , Microsoft . Reseach. dub 2013. David Foster Wallace. Mark Twain. Cowards die many times . before their deaths. . Annotated by Nelson Mandela. I . have discovered a truly marvelous proof . A CHI 2011 course. v11. Susan . Dumais. , Robin Jeffries, Daniel M. Russell, Diane Tang, Jaime . Teevan. CHI Tutorial, May, 2011. 1. Introduction. Daniel M. Russell . Google. 2. What Can We (HCI) Learn from Log Analysis? . Zhenhong. Chen, . Yanyan. . Lan. , . Jiafeng. . Guo. , Jun . Xu. , and . Xueqi. Cheng . CAS Key Laboratory of Network Data Science and Technology,. Institute . of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China. . . Chong Ho Yu. Why do we look at . grouping (cluster) patterns?. This regression model yields 21% variance explained.. The . p. value is not significant (p=0.0598). But remember we must look at (visualize) the data pattern rather than reporting the numbers. Christos Faloutsos. CMU. C. Faloutsos (CMU). 2. Roadmap. Introduction – . Motivation. Why study (big) graphs?. Part#. 1: Patterns in graphs. Part#2: Cascade analysis. Conclusions. [Extra: ebay fraud; tensors; spikes]. 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. A short tutorial…. Susan . Dumais. , Robin Jeffries, Daniel M. Russell, Diane Tang. , Jaime Teevan. HCIC Feb, 2010. What can we (HCI) learn from logs analysis? . Logs are the traces of human behavior. 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.