Abstract: Big data, described as 3Vs, high volume, high velocity, and high variety, is attracting increasing attention in database research. A prominent type of big data is graph data, where a graph (e.g. Facebook) could have billions of nodes and hundreds of billions of edges. Confronting a graph in such a scale, many existing approaches fail in graph indexing and querying. In this talk, I will present practical indexing and querying approaches for three important graph queries, i) a shortest distance query, ii) a weight constraint reachability query and iii) a top-k nearest keyword query. In addition, as an interesting byproduct of our recent research on join optimization, I will present a provably optimal external memory algorithm for triangle enumeration on large-scale networks.
Biography: Miao Qiao is currently a postdoctoral research fellow of The Chinese University of Hong Kong (CUHK), under the supervision of Prof. Yufei Tao. She received her Ph.D. degree from Department of Systems Engineering and Engineering Management at CUHK, under the supervision of Prof. Hong Cheng and Prof. Jeffrey Xu Yu in 2013. Before joining CUHK, she received her B. Eng. degree from Shanghai Jiao Tong University in 2009.