讲座题目：An Overview of Keyword Search Technologies over Heterogeneous Data

讲座时间：2015年12月10日（周四）上午10：00开始

讲座地点：计算机学院会议室（南1楼433室）

演讲者：

Zhifeng Bao

Assistant Professor (continuing position) in School of CSIT

Royal Melbourne Institute of Technology (RMIT), Australia

讲座摘要：

Data is now around every corner of our life - data is heterogeneous, of large volume and high rate of change. A very demanding task is how to make the data usable to data consumers. Data cannot make one's life better unless we provide her a way to find her expected 'needle' in such big data ocean. In this talk, I would like to give an overview of my works on improving the data usability via keyword search methodologies. In particular, we will talk about the usability and performance issues on structured data (e.g. relational data), semi-structured data (e.g. XML), unstructured data (e.g. text), spatial data, time-series data (e.g. trajectory), and graph data (e.g. social network). Data from different domains is less useful without sharing, so at the end of the talk we bring up the topic of how to enhance information sharing over the social network, for the users by the users.

讲者简介:

Zhifeng Bao is an assistant professor (continuing position) in School of CSIT, Royal Melbourne Institute of Technology (RMIT), Australia. In 2014, he was a lecturer in UTAS and affiliated with the Human Interaction Technology Lab of Australia. He received his PhD from the CS Dept at NUS in 2011. Zhifeng was the only recipient of the Best PhD Thesis Award in School of Computing. He was the winner of the Singapore IDA (Infocomm Development Authority) gold medal and prize. He has got five Best Paper Award Nominations in the past, including IEEE ICDE09, CIKM14, DASFAA12, ER14 and ASONAM13. In the last seven years, he has been committing himself to the task of "how to make data usable". He has published over 50 research papers, many of which are in prestigious big data conferences such as ACM SIGMOD, VLDB, IEEE ICDE, ACM SIGIR, CIKM, as well as top journals such as VLDB Journal, IEEE TKDE and IEEE TIP. His H-index is 10 and has a citation of 700.

In term of research problems, he focuses on exploratory search, visualized and interactive data exploration, as well as a broad interest in information retrieval problems such as fuzzy search, type-ahead search to address the mismatch between user’s search intention and the data. In term of data types, Dr. Bao’s works span across heterogeneous data, including structured data (e.g. relational data), semi-structu ed data (e.g. XML), unstructured data (e.g. text), spatial data，multimedia data (e.g. images and videos), and graph data (e.g. social network). In term of research methodology, he focused on building general yet efficient frameworks to support these usability modules, without breaking the traditional storage and indexing scheme for the underlying data.

中文简介：

鲍芝峰博士现任澳大利亚皇家墨尔本大学计算机学院助理教授（永久职位），同时为皇家墨尔本-澳洲计算所合办的大数据分析联合实验室的核心成员。2011年于新加坡国立大学取得博士学位，在35名计算机学院博士毕业生中获得最佳博士论文。同时获得新加坡信息发展局颁发的国家科技金牌。受邀在2011届计算机学院毕业典礼上做主题报告，并作为新加坡国立大学优秀毕业生代表接受新加坡总统接见。鲍博士目前有5篇论文在国际会议上获得最佳论文提名，分别是IEEE ICDE09 （International Conference on Data Engineering），DASFAA (International Conference on Database Systems for Advanced Applications)，ASONAM (ACM/IEEE Conference on Social Network Analysis and Mining)。目前在国际顶级和一流会议上发表论文50余篇，包括数据库和信息检索的顶级会议ACM SIGMOD, VLDB, IEEE ICDE, SIGIR, CIKM,顶级期刊如VLDB Journal, IEEE TKDE, IEEE TIP。 鲍博士目前论文的引用约700次，H-index为10。

研究方向：主要从事提高数据可用性的研究，具体分为四类问题：(1) 探索式搜索技术；(2) 可视化和互动式的数据探索，(3) 解决信息检索中用户搜索意图同搜索数据本身存在的各种MisMatch问题，比如：模糊近似匹配，搜索引擎的Auto-completion，(4) 搜索结果随时间变化的dynamic summarization。

研究数据类型：关于异构数据，鲍博士对结构化数据，半结构化数据，空间数据，空间文字数据，社交图谱数据的处理都有丰富的R&D经验。

研究方法：设计数据驱动的技术来提高海量异构数据的可用性，为数据分析，数据查询提供高效的基础性操作，同时保证解决方案不破坏现有的数据管理和查询系统的架构。