讲座编号：jz-yjsb-2019-y026
讲座问题：Visual Anomaly Analysis Using Correlation Graphs
主 讲 人：时磊 教授 北京航空航天大学
讲座时间：2019年05月10日（星期五）下昼14:00
讲座所在：阜成路校区西区综合楼二层西聚会室
加入工具：相关学科西席、研究生、本科生
主理单位：研究生院
承办单位：盘算机与信息工程学院，食物清静大数据手艺北京市重点实验室
主讲人简介：
时磊，现任北京航空航天大学盘算机学院大数据与脑机智能高精尖中心教授。2003、2008年于清华大学盘算机系获工学学士、博士学位。曾任中科院软件所盘算机科学国家重点实验室研究员、IBM中国研究院可视剖析组研究司理。主要研究偏向为可视剖析与数据挖掘。曾在IEEE TVCG, TKDE, VIS, ICDE, Infocom, ACM TKDD, Sigcomm, CSCW等国际顶级聚会及期刊上揭晓80余篇研究论文或图书章节。多次荣获IEEE可视剖析大会挑战赛奖项及IBM研究机构可视剖析孝顺奖。现为IEEE高级会员，VIS、KDD、IJCAI、AAAI等高水平国际聚会程序委员会委员。主持或作为主干加入国家自然科学基金、973等项目。2019年度入选北京航空航天大学青年拔尖人才支持妄想。
主讲内容：
Detecting, analyzing and reasoning anomalies is important for many real-life application domains such as computer networking, fraud analysis, and software security. The main challenges include the overwhelming number of low-risk events and their multifaceted relationships, the diversity of anomalies by various data and anomaly types, and the difficulty to incorporate domain knowledge in the anomaly analysis process. In this talk, we introduce a suite of novel concepts called correlation graphs (CG). CG achieves computational scalability, domain generality, and user interactivity through synthesizing heterogeneous types of objects, their anomalies, and multifaceted relationships in a single graph. We propose relevant anomaly detection algorithms, elaborate visualization designs, and interaction models to allow users to fully unleash the visual analytics power over the correlation graph. Case studies in real-world applications are presented that demonstrate the effectiveness of CG in the data-driven detection, visualization, and reasoning process of anomalies.