Large-Scale Probabilistic Data Management
In the era of Big Data, real-world application data often exhibit unprecedented features such as variety, volume, and velocity. Specifically, we need to deal with heterogeneous data of different formats/qualities from unreliable data sources, mange data of large scale, and search/analyze data efficiently and accurately. Therefore, in real applications such as sensor networks, location-based services, biological graph databases (e.g., gene regulatory networks), social networks, and road networks, it is very critical to efficiently extract, integrate, model, and query such large-scale, heterogeneous, and unreliable data, with the quality guarantee of data and query answers.
Dr. Xiang Lian received the Bachelor degree from the Department of Computer Science and Technology, Nanjing University, in 2003, and the PhD degree in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology, in 2009. He worked as a postdoctoral researcher in the Department of Computer Science and Engineering, Hong Kong University of Science and Technology from 2009 to 2011. He is now an assistant professor at the Department of Computer Science, University of Texas Rio Grande Valley. His research interests include query processing over (1) Probabilistic, Inconsistent, and Uncertain Databases, (2) Uncertain and Certain Graph Databases, (3) Streaming Time Series Databases, and (4) Spatial-Temporal Databases. He has published more than 58 conference/journal papers and 1 book in the area of databases. He serves as the proceeding co-chair of ACM Conference on the Management of Data (SIGMOD) in 2014 and 2015, the proceeding co-chair of International Conference on Web-Age Information Management (WAIM) in 2016, and program committee (PC) members and/or reviewers in more than 45 conferences and journals.