User-Friendly Distributed Frameworks for Processing Big Data

Abstract:

Big Data frameworks emphasize two aspects, "programming simplicity" and "efficiency". The aim is to write a distributed algorithm in just a few lines of code and to let the underlying execution engine fully utilize the hardware (CPUs, disks and the network) of a cluster. Examples include Google's MapReduce, Pregel, and TensorFlow.

This talk will introduce a number of graph-analytics frameworks developed in my group, including data-intensive vertex-centric frameworks for graph analytics (e.g., for computing PageRanks, connected components) where users think like a vertex when writing programs, and a framework for compute-intensive graph analytics (e.g., community detection, subgraph matching) where users think like a subgraph. Demos of the frameworks will be provided to show their efficiency.

Bio:

Da Yan is currently an assistant professor at the Department of Computer Science, the University of Alabama at Birmingham. He is the sole winner of Hong Kong 2015 Young Scientist Award in Physical/Mathematical Science. He developed a number of high-impact systems for Big Graph analytics, which are up to orders of magnitude faster than their competitors. He frequently publishes research papers in first-tier conferences and journals like PVLDB, SIGMOD, ICDE, SIGKDD, WWW, SoCC, TKDE, TPDS, and is the 1st author of two books on Big Graph analytic published in "Foundations and Trends in Databases" and "Springer Briefs in Computer Science". More information about Dr. Yan’s research can be found at http://www.cs.uab.edu/yanda

Last Updated: Nov 10, 2021