PPT-Graph Data Mining with Map-Reduce

Author : liane-varnes | Published Date : 2016-04-07

Nima Sarshar PhD INTUIT Inc Nimasarsharintuitcom Intuit Graphs and Me Me Largescale graph data processing complex networks analysis graph algorithms Intuit

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Graph Data Mining with Map-Reduce: Transcript

Nima Sarshar PhD INTUIT Inc Nimasarsharintuitcom Intuit Graphs and Me Me Largescale graph data processing complex networks analysis graph algorithms Intuit QuickBooks TurboTax . Data Analysis on . MapReduce. Chao Liu, Hung-. chih. Yang, Jinliang Fan, Li-Wei He, Yi-Min Wang. Internet Services Research Center (ISRC). Microsoft Research Redmond. Internet Services Research Center (ISRC). Origins & Applications. Christopher Smith. Xavier Stevens. John Carnahan. Original Map & Reduce. LISP. map f(x) [x. 0. , x. 1. , …, x. n. ]. yields: [f(x. 0. ), f(x. 1. ), …, f(x. n. )]. reduce f(x, y) [x. “MapReduce and parallel DBMSs: friends or foes?” Stonebraker, Daniel Abadi, David J Dewitt et al.. MAP REDUCE AND PARALLEL DBMS ARE COMPLEMENTARY. In 2010, . MapReduce. (MR) has been hailed as a . Science Applications. http://salsahpc.indiana.edu . School of Informatics and Computing. Indiana University. Application Classes. 1. Synchronous. Lockstep Operation as in SIMD architectures. SIMD. 2. Thilina Gunarathne (. tgunarat@indiana.edu. ). Judy . Qiu (. xqiu@indiana.edu. ). Dennis . Gannon (. dennis.gannon@microsoft.com). Introduction. Three disruptions. Big Data. MapReduce. Cloud Computing. Zaharia, . Mosharaf. . Chowdhury. , . Tathagata. Das,. Ankur. Dave, Justin Ma, Murphy McCauley, Michael Franklin,. Scott . Shenker. , Ion . Stoica. Spark. Fast, Interactive, Language-Integrated Cluster Computing. ish. ) Frameworks. William Cohen. 1. Outline. More concise languages for map-reduce pipelines. Abstractions built on top of map-reduce. General comments. Specific systems. Cascading, Pipes. PIG, Hive. Basics. Divide and conquer. Partition large problem into smaller . subproblems. Worker work on . subproblems. in parallel. Threads in a core, cores in multi-core processor, multiple processor in a machine, machines in a cluster. Chapter 7 : Advanced Frequent Pattern Mining. Jiawei Han, Computer Science, Univ. Illinois at Urbana-Champaign. , 2017. 1. October 28, 2017. Data Mining: Concepts and Techniques. 2. Chapter 7 : Advanced Frequent Pattern Mining. Professor Tom . Fomby. Director. Richard B. Johnson Center for Economic Studies. Department of Economics. SMU. May 23, 2013. Big Data:. Many Observations on Many Variables . Data File. OBS No.. Target Var.. 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. Big Data Infrastructure Week 5: Analyzing Graphs (2/ 2) This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 United States See http://creativecommons.org/licenses/by-nc-sa/3.0/us/ for details Madhu M Nayak Assistant Professor, Department of C SE, GSSSIETW, Mysuru Pradeep.S Assistant Professor, Department of C SE, GEC, Kushal Nagar Abstract - Due to the increasing popularity of cheap http://www.cs.uic.edu/~. liub. CS583, Bing Liu, UIC. 2. General Information. Instructor: Bing Liu . Email: liub@cs.uic.edu . Tel: (312) 355 1318 . Office: SEO 931 . Lecture . times: . 9:30am-10:45am.

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