万搏网页版学术报告
报告一
题目：Enriched Geo-Spatial Data Search and Analysis
报告人：Nikos Mamoulis教授（香港大学）
时间：2014年6月16日（星期一）14:00
地点：校本部理工楼504会议室
报告摘要：Geospatial data nowadays are associated with additional information of multiple and complex types. For example, spatial locations can be associated with users from social networks that visit these places, with textual annotations, with multimedia information such as photographs, or with scientific data. In addition, the data could carry temporal information, or they could be uncertain. In this talk, I will present some of the recent works by our group on analyzing such enriched geo-spatial data.
报告人简介：Nikos Mamoulis is a professor at the Department of Computer Science, University of Hong Kong, which he joined in 2001. His research focuses on the management and mining of complex data types, including spatial, spatio-temporal, object-relational, multimedia, text and semi-structured data. He has served on the program committees of over 90 international conferences and workshops on data management and data mining. He was the general chair of SSDBM 2008 and the PC chair of SSTD 2009. He is an associate editor for IEEE TKDE and the VLDB Journal.
报告二：
题目：A Partition-Based Approach to Structure Similarity Search
报告人：赵翔博士（国防科学技术大学）
时间：2014年6月16日（星期一）15:30
地点：校本部理工楼504会议室
报告摘要：Graphs are widely used to model complex data in many applications, such as bioinformatics, chemistry, social networks, pattern recognition, etc. A fundamental and critical query primitive is to efficiently search similar structures in a large collection of graphs. This talks concerns graph similarity queries with edit distance constraints. Existing solutions to the problem utilize fixed-size overlapping substructures to generate candidates, and thus become susceptible to large vertex degrees or large distance thresholds. In this talk, we present a partition-based approach to tackle the problem. By dividing data graphs into variable-size non-overlapping partitions, the edit distance constraint is converted to a graph containment constraint for candidate generation. We develop efficient query processing algorithms based on the new paradigm. A candidate pruning technique and an improved graph edit distance algorithm are also developed to further boost the performance. In addition, a cost-aware graph partitioning technique is devised to optimize the index. Extensive experiments demonstrate the proposal significantly outperforms existing approaches.
报告人简介：赵翔，国防科学技术大学信息系统与管理学院讲师，1986年生。2008年6月于国防科学技术大学获得工学学士学位；2009年5月获国家留学基金委“建设高水平大学公派研究生项目”资助；2013年8月于澳大利亚新南威尔士大学获得计算机科学与工程博士学位。现从事信息资源管理与应用方面的研究，重点是网络（图）结构数据等大数据管理与应用。在国际学术刊物和会议上发表论文十余篇，包括数据管理领域顶级会议SIGMOD、VLDB和ICDE等，多次参与国际学术会议并做口述报告。参与编著国家级规划教材1本，担任众多国际会议和期刊的审稿人。是ACM、IEEE和CCF会员。