Abstract

Graph data are of growing importance in many applications, including the semantic web (i.e., RDF), social network analysis, bioinformatics, software engineering, e-commerce, finance and trading, fraud detection, and recommendation systems, because they model complicated structures and relationships well. The size and complexity of these graphs raise significant data management and data analysis challenges. This has led to a number of different algorithms and approaches to graph processing as well as systems that are based on these algorithms. In this presentation, I will focus on the platforms that have been developed to facilitate graph analytics. I will start with a classification of various approaches and systems, and then discuss the systems that have been developed to facilitate graph analytics.

Biography

M. Tamer Özsu is Professor of Computer Science at the David R. Cheriton School of Computer Science. His research is in data management focusing on large-scale data distribution and management of non-traditional data. He is a Fellow of the Royal Society of Canada, American Association for Advancement of Research (AAAS), the Association for Computing Machinery (ACM), and the Institute of Electrical and Electronics Engineers (IEEE). He is an elected member of the Science Academy of Turkey, and member of Sigma Xi and American Association for the Advancement of Science (AAAS).