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Temporal Skeletonization on Sequential Data: Patterns, Categorization, and Visualization

模式识别学术大讲堂

Advanced Lecture Series in Pattern Recognition

    (TITLE)Temporal Skeletonization on Sequential Data: Patterns, Categorization, and Visualization

(SPEAKER)Prof. Hui Xiong (Rutgers, the State University of New Jersey)

(CHAIR)Prof. Wang Liang

     (TIME):August 13(Wednesday), 2014, 10:00AM

    (VENUE):No.2 Conference Room (3rd floor), Intelligence Building

报告摘要(ABSTRACT):

Sequential pattern analysis targets on finding statistically relevant temporal structures where the values are delivered in a sequence. With the growing complexity of real-world dynamic scenarios, it often requires more and more symbols to encode a meaningful sequence. This is so-called the “curse of cardinality” problem, which can impose significant challenges to the design of sequential analysis methods in terms of computational efficiency and practical use. Indeed, given the overwhelming scale and the heterogeneous nature of the sequential data, what is needed is a new vision and strategy to face the challenges. To this end, in this talk, we introduce a temporal skeletonization approach to proactively reduce the representation of sequences, so as to expose their hidden multi-level temporal structures. The key idea is to summarize the temporal correlations in an undirected graph. Then, the “skeleton" of the graph serves as a higher granularity on which hidden temporal patterns are more likely to be identified. As a matter of fact, the embedding topology of the graph can allow to translate the rich temporal content into metric space. This opens up new possibilities to explore, quantify, and visualize sequential data in the metric space. Finally, experimental results on real-world data have shown that the proposed approach can greatly alleviate the problem of curse of cardinality for the challenging tasks of sequential pattern mining and clustering. Also, the evaluation on a Business-to-Business (B2B) marketing application demonstrates that our approach can effectively discover critical buying paths from noisy marketing data.

报告人简介(BIOGRAPHY):

Dr. Hui Xiong is currently a Professor and the Vice Chair of the Management Science and Information Systems Department, and the Director of Rutgers Center for Information Assurance at Rutgers, the State University of New Jersey, where he received a two-year early promotion/tenure (2009), the Rutgers University  Board of Trustees Research Fellowship for Scholarly Excellence (2009), and the ICDM-2011 Best Research Paper Award (2011). Dr. Xiong received his Ph.D. in Computer Science from the University of Minnesota (UMN), USA, in 2005, the B.E. degree in Automation from the University of Science and Technology of China (USTC), China, and the M.S. degree in Computer Science from the National University of Singapore (NUS), Singapore. His general area of research is data and knowledge engineering, with a focus on developing effective and efficient data analysis techniques for emerging data intensive applications. He has published prolifically in refereed journals and conference proceedings (3 books, 60+ journal papers, and 60+ conference papers). He is the co-Editor-in-Chief of Encyclopedia of GIS by Springer, and an Associate Editor of IEEE Transactions on Knowledge and Data Engineering (TKDE) as well as the Knowledge and Information Systems (KAIS) journal. He has served regularly on the organization and program committees of numerous conferences, including as a Program Co-Chair of the Industrial and Government Track for the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), a Program Co-Chair for the IEEE 2013 International Conference on Data Mining (ICDM-2013), and a General Chair for the IEEE 2015 International Conference on Data Mining(ICDM-2015.)

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中科院自动化研究所 模式识别国家重点实验室
NLPR, INSTITUTE OF AUTOMATION, CHNESE ACADEMY OF SCIENCES