||Professor, Swinburne University of Technology, Australia
||2019 年 12 月 7 日（周六）上午 9：30
||张江校区软件楼 102 第二会议室
In the era of big data, we face the challenge on how to find effective and efficient solutions for managing, integrating and analyzing complex data with high volume, variety and velocity. In this talk, I will introduce the research we have been carrying out in the Web and Data Engineering group at Swinburne University in Australia. In particular, I will introduce some of our work on keyword search over structured data, community search over large networks, and advanced query processing and refinement supported by several ARC (Australia Research Council) discovery projects.
Chengfei Liu is a Professor and the head of Web and Data Engineering research group in the Department of Computer Science and Software Engineering at Swinburne University of Technology, Australia. He received the BS, MS and PhD degrees in Computer Science from Nanjing University, China in 1983, 1985 and 1988, respectively. Prior to joining Swinburne, he taught at the University of South Australia and the University of Technology Sydney, and was a Senior Research Scientist at Cooperative Research Centre for Distributed Systems Technology (DSTC) located at University of Queensland. He also held visiting positions at the Chinese University of Hong Kong, the University of Aizu in Japan, and IBM Silicon Valley Lab in USA. His current research interests include graph data management over large networks, keyword search on structured data, query processing and refinement for advanced database applications, and data-centric workflows. He has attracted around 6.5 million dollars in competitive research grants (mostly awarded by the Australia Research Council – ARC) and published more than 230 papers in prestigious journals and conference proceedings, such as ACM TODS, IEEE TKDE, VLDB Journal, SIGMOD, VLDB, ICDE, WWW, EDBT, WSDM, CIKM. He has also served in over 130 organization committees and program committees in international conferences/workshops, including SIGMOD, VLDB, ICDE, EDBT, CIKM, ICDM, and ICSOC.