Big Graph Analytics Systems [Tutorial 2]
SUNDAY, June 26, 2016 (1:30pm - 5:00pm)
Abstract: In recent years we have witnessed a surging interest in developing Big Graph processing systems. To date, tens of Big Graph systems have been proposed. This tutorial provides a timely and comprehensive review of existing Big Graph systems, and summarizes their pros and cons from various perspectives. We start from the existing vertex-centric systems, which which a programmer thinks intuitively like a vertex when developing parallel graph algorithms. We then introduce systems that adopt other computation paradigms and execution settings. The topics covered in this tutorial include programming models and algorithm design, computation models, communication mechanisms, out-of-core support, fault tolerance, dynamic graph support, and so on. We also highlight future research opportunities on Big Graph analytics.
URL for the Slides:
Dr. YAN, Da is currently with the Department of Computer Science and Engineering at the Chinese University of Hong Kong. His research focuses on developing scalable systems and algorithms for Big Data Analytics. He is the winner of the 2015 Hong Kong Young Scientist Award, and will join the University of Alabama at Birmingham as a faculty member in Fall 2016.
Dr. Yingyi Bu is a senior software engineer in Couchbase Inc. He is the recipient of a Google PhD fellowship award and a Yahoo! key scientific challenge award.
Dr. Yuanyuan Tian is a Research Staff Member at IBM Almaden Research Center, USA. Her research interests include large scale machine learning, graph analytics, SQL on Hadoop, and big data federation. She is the recipient of the Distinguished Achievement Award from the Univeristy of Michigan.
Dr. Amol Deshpande is an Associate Professor of Computer Science at the University of Maryland at College Park. His research interests include graph data management, adaptive query processing, data streams, sensor networks and statistical modeling of data. He is a recipient of the NSF CAREER Award.
Dr. James Cheng is with the Department of Computer Science and Engineering at the Chinese University of Hong Kong. His research focuses on big data infrastructures, distributed computing systems, and large-scale network analysis.