Keynote Speaker---Prof. Sheng-Lung Peng
Prof. Sheng-Lung Peng, Department of Computer Science and Information Engineering, National Dong Hwa University, Taiwan
Sheng-Lung Peng is a Professor of the Department of Computer Science and Information Engineering at National Dong Hwa University, Hualien, Taiwan. He received the BS degree in Mathematics from National Tsing Hua University, and the MS and PhD degrees in Computer Science from the National Chung Cheng University and National Tsing Hua University, Taiwan, respectively. He is an honorary Professor of Beijing Information Science and Technology University of China and a visiting Professor of Ningxia Institute of Science and Technology of China. He serves the regional director of the ICPC Contest Council for Taiwan region, a director of Institute of Information and Computing Machinery, of Information Service Association of Chinese Colleges and of Taiwan Association of Cloud Computing. He is also a supervisor of Chinese Information Literacy Association, of Association of Algorithms and Computation Theory, and of Interlibrary Cooperation Association in Taiwan. His research interests are in designing and analyzing algorithms for Bioinformatics, Combinatorics, Data Mining, and Networks. Dr. Peng has edited several special issues at journals, such as Soft Computing, Journal of Real-Time Image Processing, Journal of Internet Technology, Journal of Computers, MDPI Algorithms, and so on. He published over 100 international conferences and journal papers.
On the k-dominant Skyline Problem
Given a d-dimensional data set D, a point p dominates point q if it is better than or equal to q in all dimensions and better than q in at least one dimension. A point is a skyline point if there is no other point that can dominate it. The skyline problem is to output all the skyline points from D. The skyline points are useful in many decision making applications. Unfortunately, as the number of dimensions increases, the number of skyline points becomes too large since the chance of one point dominating another one is very low. As a result, these skyline points cannot offer any interesting insights. To find more important and meaningful skyline points in high dimensional space becomes a challenge work. A new concept, called k-dominant skyline which relaxes the idea of dominance to k-dominance is proposed. A point p is said to k-dominate point q if there are k (≤ d) dimensions in which p is better than or equal to q and is better in at least one of these k dimensions. A point that is not k-dominated by any other points is in the k-dominant skyline. In this talk, we present some state-of-art works for the k-dominant skyline problem. In particular, an algorithm of maintaining k-dominant skyline in a dynamic environment is proposed. By keeping some information of the original k-dominant skylines, we can compute the changing skyline efficiently.