Abstract:

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 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 usability over heterogeneous data. 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), multimedia data (e.g. images and videos), 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.

 

Zhifeng Bao is an assistant professor in School of CS & IT, RMIT university, 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 and was the winner of the Singapore IDA (Infocomm Development Authority) gold medal. In the last seven years, he has been committing himself to the task of \"how to make data usable\", and enhance data & knowledge sharing over the social network. His \"data usability\" works span across heterogeneous data, including structured data (e.g. relational data), semi-structured 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). 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.