Seminar: Knowledge Cube Construction from Massive Social Sensing Data

Event Date: 
Mon, 2018-03-19 10:00 - 11:15

Location: McBryde 655
Speaker: Chao Zhang

Social sensing data are massive and ubiquitous. Effective and scalable analytics of social sensing data can be game changing for urban science, business, healthcare, and homeland security. However, such data pose great challenges to computer science research since they are often unstructured, fragmented, noisy, and intermingled with rich contexts. In this talk, I will introduce a systematic framework, KnowCube, that addresses the above challenges by turning unstructured, noisy social sensing data into a sturctured, multidimensional knowledge cube. In particular, I will discuss in detail how to solve two key problems for knowledge cube contsturction: (1) how to extract events from noisy social sensing data; and (2) how to organize unstructured events into a multidimensional cube structure without supervision. KnowCube serves as a versatile and easy-to-use knowledge engine that can harness the power of social sensing for many applications. Finally, I will share some future research future research directions on better knowledge cube construction and building next-generation intelligent systems with the knowledge cube.

Chao Zhang is a PhD candidate in Computer Science at University of Illinois at Urbana-Champaign, advised by Professor Jiawei Han. His research lies in data mining and machine learning, focusing on mining knowledge from social sensing data for building intelligent systems. His research is at the intersection of social media analysis, text mining, spatiotemporal data mining, and graph mining. Chao has published more than 30 papers in KDD, WWW, SIGIR, VLDB, AAAI, WSDM, ECML/PKDD, CIKM, ICDE, TKDE, TIST, and others. He is the recipient of the ECML/PKDD Best Student Paper Runner-up Award (2015) and the Chiang Chen Overseas Graduate Fellowship (2013). His developed technologies have received multiple media coverages and been transferred to Microsoft and US Army Research Lab.