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墨尔本皇家理工大学 Zhifeng Bao博士学术讲座预告

作者:DMIR    来自:    发表时间:2016-10-17    浏览量:438


报告时间2016年10月18日(周二)上午1030

报告地点:广工大学城校园工学一号馆216室(学术报告厅)

报告题目Exploring the Heterogeneous Data for Enhanced Usability

报告人:墨尔本皇家理工大学 Zhifeng Bao博士

报告人简介:

      Zhifeng Bao is an assistant professor in School of Computer Science & IT, RMIT University, Australia. He received his PhD from the CS Dept at National University of Singapore in 2011. Zhifeng was the recipient of the Best PhD Thesis Award in School of Computing and was the winner of the Singapore IDA (Infocomm Development Authority) gold medal. His research was supported by various researching funding such as Google faculty research grant. He has been committing himself to the task of "how to make data usable", including structured data (e.g. relational data), semi-structured data (e.g. XML), unstructured data (e.g. text), spatial data and graph data (e.g. social network). 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. He has served the program committee of top conferences in Database and Information Retrieval such as VLDB, ICDE, SIGIR, as well as demo track chair of APWEB16 and workshop track chair of DASFAA17. He got Four Best Paper Award Nominations in ICDE 2009, DASFAA 2012, CIKM 2014, ER 2014, and most recently he was awarded the Best Student paper Award and the Best Demo Paper Award in ADC 2016.

报告摘要:      

Big 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 ones 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 usability over heterogeneous data. In particular, we will talk about the usability and performance issues on structured data (mismatch problem), spatial-textual data (fuzzy type-ahead search problem), spatio-temporal data (activity trajectory search problem), and graph data (e.g. personalized realtime search over social media). Last I will introduce the system we are building for an interactive and visualized exploration of the location-centered real estate data in Australia for the last ten years.

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