PPT-1 Tree and Graph Processing

On Hadoop Ted Malaska 2 Schedule Intro Overview of Hadoop and EcoSystem Summarize Tree Rooting MR OverviewImplementation Options Hbase OverviewImplementation Options Giraph OverviewImplementation Options. By vU we denote the maximum distance from vertex to any vertex in For a vertex the eccentricity of is the maximum of the distance to any vertex in the graph ie max vu or vV The diameter of a graph is the maximum of the eccentricity of any vertex of auxiliary . storage. Gennaro. . Parlato. . (University of Southampton, UK). Joint work: . P. . Madhusudan. – UIUC, USA . . . Automata with aux storage. Turing machines = finite automata + 1 infinite tape. Implement a graph in three ways:. Adjacency List. Adjacency-Matrix. Pointers/memory for each node (actually a form of adjacency list). Adjacency List. List of pointers for each vertex. Undirected Adjacency List. Arijit Khan, . Yinghui. Wu, Xifeng Yan. Department of Computer Science. University of California, Santa Barbara. {. arijitkhan. , . yinghui. , . xyan. }@. cs.ucsb.edu. Graph Data. 2. Graphs are everywhere.. Farzad Khorasani. , . Rajiv Gupta. , . Laxmi. N. . Bhuyan. University of California Riverside. Scalable SIMD-Efficient Graph Processing on GPUs. Graph Processing. Building blocks of data analytics.. By Clara Bryant. How a Human solves a Maze. Greek Myth of the Minotaur. Greek hero Theseus is given string to find his way through and back out of the maze. String and chalk method to solve a maze. Mark the places you’ve visited already . - Week 13. 2. Problem: Laying Telephone Wire. Central office. 3. Wiring: Naive Approach. Central office. Expensive!. 4. Wiring: Better Approach. Central office. Minimize the total length of wire connecting . Hao Wei. 1. , . Jeffrey Xu Yu. 1. , Can L. u. 1. , . Xuemin. Lin. 2. . 1 . The . Chinese University of Hong Kong, Hong Kong. 2 . The . University of New South Wales. , . Sydney, Australia. Graph in Big Data . Richard Peng. Georgia Tech. Based on . recent works . joint with:. Serban . Stan (Yale. ), . Haoran. . Xu (MIT. ),. Shen . Chen Xu (CMU. ), . Saurabh. . Sawlani. (. GaTech. ). John . Gilbert (UCSB. Why graph clustering is useful?. Distance matrices are graphs .  as useful as any other clustering. Identification of communities in social networks. Webpage clustering for better data management of web data. William Cohen. 1. Announcements. Next Tuesday 12/8:. Presentations for 10-805 projects.. 15 minutes per. project.. Final written reports due Tues 12/15. 2. Graph-Based Parallel Computing. William Cohen. Distance matrices are graphs .  as useful as any other clustering. Identification of communities in social networks. Webpage clustering for better data management of web data. Outline. Min s-t cut problem. Stijn Eyerman, Wim Heirman, Ibrahim Hur, Joshua B. Fryman. 2. DARPA HIVE project. “To build a graph analytics processor that can process streaming graphs 1000X faster and at much lower power than current processing technology”. The Problem . Large Graphs are often part of computations required in modern systems (Social networks and Web graphs etc.). There are many . graph . computing problems like shortest path, clustering, page rank, minimum cut, connected components .