Topics on Uncertain Data Streams
演講標題:Topics on Uncertain Data Streams

演 講 者:Prof. Chuan-Ming Liu 劉傳銘
National Taipei University of Technology (NTUT)

時 間:2016/11/22(二) 11:00-13:00

地 點:交大工程四館816室

參加對象:全校老師及學生

報名方式:
1. 人數40人
2. 報名網址:https://goo.gl/forms/cPAnfyDLXf3eyNZj1

主辦單位:交大-IBM 智慧物聯網巨量資料研發中心

協辦單位:邁向頂尖大學計畫智慧資通訊研究中心、電機系

聯絡方式:寸小姐
電話:03-5712121分機54599
Email:nellycun@g2.nctu.edu.tw

摘要

As the number of mobile and wearable devices dramatically increases recently, the amount of data acquired in the cloud applications with these devices becomes huge and creates new problems in big data. The derived data may be imprecise or uncertain due to the transmission loss or the error of the devices. In addition, the values of data vary as time evolves. Such data are referred to as uncertain data streams. With the demand of real-time process in big data, how to effectively manage, process, or analyze the data on uncertain data streams become crucial. In this talk, related topics about query processing for some important query types over uncertain data streams will be introduced, including probabilistic k nearest neighbors search, probabilistic reverse k nearest neighbors search, probabilistic Top-k dominating query, probabilistic skyline query, and probabilisticεJoin. In particular, an effective approach for probabilistic skyline query over uncertain data streams will be discussed.


簡歷

Chuan-Ming Liu is a professor in the Department of Computer Science and Information Engineering, National Taipei University of Technology (NTUT), TAIWAN. He received his Ph. D. in Computer Sciences from Purdue University in 2002 and B.S. and M.S. degrees both in Applied Mathematics from National Chung-Hsing University, Taiwan, in 1992 and 1994, respectively. In the summer of 2010 and 2011, he has held visiting appointments at Auburn University and Beijing Institute of Technology, respectively. Dr. Liu’s research interests include data management and data dissemination in various emerging computing environments, query processing in moving objects, location-based services, big data processing, ad-hoc and sensor networks, parallel and distributed computation.

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