Towards Processing of Big Graphs

Abstract

Graphs are very important parts of Big Data and widely used for modelling complex structured data with a broad spectrum of applications such as bioinformatics, web search, social network, road network, etc. Over the last decade, tremendous research efforts have been devoted to many fundamental problems in managing and analysing graph data. In this talk, I will present some of our recent research efforts in processing big graphs including scalable processing theory and techniques, distributed computation, and system framework.

Biography

Xuemin Lin is a UNSW Scientia Professor and the head of database group in the school of computer science and engineering at UNSW, Australia. Xuemin’s research interests lie in databases, algorithms, and complexities. Specifically, he is working in the area of scalable data processing covering graph data, spatial-temporal data, streaming data, uncertain data, text data, etc. Xuemin was an associate editor of ACM TODS (2008-2014), IEEE TKDE (Feb 2013- Jan 2015), and an associate editor-in-Chief of IEEE TKDE (2015-2016). He is currently the Editor-in-Chief of IEEE TKDE (2017 Jan – Now). Xuemin has published over 130 papers in the top venues such as SIGMOD, SIGIR, SIGKDD, ACM MM, VLDB, PODS, ICDE, IJAIC, IEEE TKDE, VLDB J, and ACM TODS. Xuemin co-authored 16 best papers in the international conferences, including the best paper award in ICDE2016 and best student paper award in ICDE2007. Xuemin Lin was selected as one of the National Thousand Distinguished Overseas Scholars in China in 2010. He is an IEEE Fellow.