||Big Data Management for Data Intensive Computing
Big data now widely occurs in science research and engineering practices, which brings up a new discipline, Data Intensive Science and Engineering (DISE). Generally, big data refers to a data set with a size of hundreds of TB, or several PB or even above, and it is often distributed, heterogeneous and in low-quality. It is critical to devise novel methods to manage big data since traditional database management techniques are unfeasible to manage big data efficiently and effectively, though such techniques, especially the commercial relational DBMSs, have achieved great success in the past decades. DISE covers contents from data storage and organization, computational method, data analysis, to user interfaces. In this talk, we will discuss the challenges and our current work on big data management briefly.
Speaker: Xiaoling Wang is currently Professor of Computing Science at the School of Software Engineering at East China Normal University, China. She is interested in researching and developing effective and efficient data management and analysis techniques for data intensive applications. She is currently working on various techniques of XML data management, data mining, Web search, information retrieval and web services. Her research has been extensively supported in part by governmental funding agencies and industry partners. She has published over 50 research papers in journals and conferences. She has served in the program committees of over 20 international conferences and workshops.