PPT-CSCI 6900: Mining Massive Datasets

Shannon Quinn with thanks to William Cohen of Carnegie Mellon and Jure Leskovec of Stanford Big Data Astronomy Sloan Digital Sky Survey New Mexico 2000 140TB over 10 years Large Synoptic Survey Telescope. Eileen Kraemer. August 24. th. , 2010. The University of Georgia. Java Threads & Concurrency, continued. Liveness. Deadlock. Starvation and . Livelock. Guarded Blocks. Immutable Objects. A Synchronized Class Example. Shannon Quinn. (with content graciously and viciously borrowed from William Cohen’s 10-605. Machine Learning with Big Data and Stanford’s MMDS MOOC . http://www.mmds.org/. ). “Big Data”. Astronomy. MMDS . Secs. . 3.2-3.4. . Slides adapted from: . J. . Leskovec. , A. . Rajaraman. , J. Ullman: Mining of Massive Datasets, . http://www.mmds.org. October 2014. Task: Finding . Similar Documents. Goal:. (Part . 2). Mining of Massive Datasets. Jure Leskovec, . Anand. . Rajaraman. , Jeff Ullman . Stanford University. http://www.mmds.org . Note to other teachers and users of these . slides:. We . would be delighted if you found this our material useful in giving your own lectures. Feel free to use these slides verbatim, or to modify them to fit your own needs. Jeffrey Miller, Ph.D.. jeffrey.miller@usc.edu. Outline. Conditions. Program. USC CSCI 201L. Conditional Statements. Java has three conditional statements, similar to C . if-else. switch-case. Conditional ternary operator . SVD & CUR. Mining of Massive Datasets. Jure Leskovec, . Anand. . Rajaraman. , Jeff Ullman . Stanford University. http://www.mmds.org . Note to other teachers and users of these . slides:. We . would be delighted if you found this our material useful in giving your own lectures. Feel free to use these slides verbatim, or to modify them to fit your own needs. CS246: Mining Massive Datasets. Jure Leskovec, . Stanford University. http://cs246.stanford.edu. Recap: Finding similar documents. Task:. . Given a large number (. N. in the millions or billions) of documents, find “near duplicates”. t. e. n. t. -based Systems & Collaborative Filtering. Mining of Massive Datasets. Jure Leskovec, . Anand. . Rajaraman. , Jeff Ullman . Stanford University. http://www.mmds.org . Note to other teachers and users of these . CS246: Mining Massive Datasets. Jure Leskovec, . Stanford University. http://cs246.stanford.edu. Recap: Finding similar documents. Task:. . Given a large number (. N. in the millions or billions) of documents, find “near duplicates”. Decision Trees on MapReduce CS246: Mining Massive Datasets Jure Leskovec, Stanford University http://cs246.stanford.edu Decision Tree Learning Give one attribute (e.g., lifespan), try to predict the value of new people’s lifespans by means of some of the other available attribute Frequent Itemset Mining & Association Rules Mining of Massive Datasets Jure Leskovec, Anand Rajaraman , Jeff Ullman Stanford University http://www.mmds.org Note to other teachers and users of these Ranking Nodes on the Graph. Web pages are not equally “important”. www.joe-schmoe.com. vs. . www.stanford.edu. . Since there is large diversity . in the connectivity of the . web graph we can . S201David GoldschmidtEmail goldschmidtgmailcomOffice Amos Eaton 115Office hours Mon 930-1100AMTue 1100AM-1230PMThu 200-300PMKonstantin KuzminEmail kuzmik2rpieduOffice Amos Eaton 112Office hours TBDGra Bar-coding Cages. AR starting using barcodes for tracking census in December of 2005. Why bar-coding?. Provide timely census data. Provide accurate census data. Track room capacity. Each cage card/bar code is specific to the following information .