Keynote Speakers & Abstracts

  

    Prof. Philip S. Yu

Keynote title: Exploring Big Data for Logistic Planning in the Sharing Economy: A Case Study on the Bike Sharing System

Abstract:

The sharing economy enables people to efficiently get what they need when and where they want them. There are many successful examples, including Uber, Didi Chuxing, Airbnb, Ofo, DriveNow, etc. All these companies would not be viable businesses without leveraging a platform and a foundation of big data. These companies don’t just represent a new way of thinking or new services, but a new way to use data effectively to provide efficient services. In this talk, we use a bicycle-sharing system, which can provide shared bike usage services for the public, as a case study on exploring big data for logistic planning of better services. In bicycle-sharing systems, people can borrow and return bikes at any stations in the service region very conveniently. Therefore, bicycle-sharing systems are normally used as a short distance trip supplement for private vehicles as well as regular public transportation. Meanwhile, for stations located at different places in the service region, the bike usages can be quite skewed and imbalanced. Some stations have too many incoming bikes and get jammed without enough docks for upcoming bikes, while some other stations get empty quickly and lack enough bikes for people to check out. We will discuss the various logistic issues and solutions that utilize data to improve service quality.


Bio.:

Philip S. Yu's main research interests include big data, data mining (especially on graph/network mining), social network, privacy preserving data publishing, data stream, database systems, and Internet applications and technologies. He is a Distinguished Professor in the Department of Computer Science at UIC and also holds the Wexler Chair in Information and Technology. Before joining UIC, he was with IBM Thomas J. Watson Research Center, where he was manager of the Software Tools and Techniques department. Dr. Yu has published more than 970 papers in refereed journals and conferences with more than 74,500 citations and an H-index of 127. He holds or has applied for more than 300 US patents.

Dr. Yu is the Editor-in-Chief of ACM Transactions on Knowledge Discovery from Data.  He is on the steering committee of ACM Conference on Information and Knowledge Management and was a steering committee member of the IEEE Data Engineering and the IEEE Data Mining Conference.  He was the Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering (2001-2004). He received the ICDM 2013 10-year Highest-Impact Paper Award, and the EDBT Test of Time Award (2014). He had received several IBM honors including 2 IBM Outstanding Innovation Awards, an Outstanding Technical Achievement Award, 2 Research Division Awards and the 94th plateau of Invention Achievement Awards.  He was an IBM Master Inventor. Dr. Yu received his PhD from Stanford University.



 

    Prof. Jeffrey Xu Yu

Keynote title: To Speedup Graph Algorithms

Abstract:

The CPU cache performance is one of the key issues to efficiency in database systems. It is reported that cache miss latency takes a half of the execution time in database systems. In this talk, we focus on CPU speedup for graph computing in general by reducing the CPU cache miss ratio for different graph algorithms. We explore a general approach to speed up CPU computing, in order to further enhance the efficiency of the graph algorithms without changing the graph algorithms (implementations) and the data structures used. That is, we aim at designing a general solution that is not for a specific graph algorithm, neither for a specific data structure. The approach studied in this work is graph ordering, which is to find the optimal permutation among all nodes in a given graph by keeping nodes that will be frequently accessed together locally, to minimize the CPU cache miss ratio. We prove the graph ordering problem is NP-hard, and give a basic algorithm with a bounded approximation. To improve the time complexity of the basic algorithm, we further propose a new algorithm to reduce the time complexity and improve the efficiency with new optimization techniques based on a new data structure. We conducted extensive experiments to evaluate our approach in comparison with other possible graph orderings. We confirm that our approach can achieve high performance by reducing the CPU cache miss ratios.


Bio.:

Dr Jeffrey Xu Yu is a Professor in the Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong. His current main research interests include graph mining, graph query processing, graph pattern matching, keywords search in databases, and online social networks. Dr. Yu served as an Information Director and a member in ACM SIGMOD executive committee (2007-2011), an associate editor of IEEE Transactions on Knowledge and Data Engineering (2004-2008), an associate editor in VLDB Journal (2007-2013), and the steering committee chair of APWeb (2013-2016). Currently he serves as an associate editor of ACM Transactions on Database Systems (TODS), WWW Journal, the International Journal of Cooperative Information Systems, the Journal on Health Information Science and Systems (HISS), and Journal of Information Processing. Dr. Yu served/serves in many organization committees and program committees in international conferences/workshops including PC Co-chair of APWeb'04, WAIM'06, APWeb/WAIM'07, WISE'09, PAKDD'10, DASFAA'11, ICDM'12, NDBC'13, ADMA'14, CIKM'15, and Bigcomp17.



 

    Prof. Ning Zhong

Keynote title: Brain Big Data Based Wisdom Service: A Brain Informatics Based Systematic Approach

Abstract:

In this talk, I demonstrate a Brain Informatics based systematic approach to an integrated understanding of macroscopic and microscopic level working principles of the brain by means of experimental, computational, and cognitive neuroscience studies, as well as utilizing advanced Web intelligence centric information technologies. I discuss research issues and challenges with respect to brain big data computing from three aspects of Brain Informatics studies that deserve closer attention: systematic investigations for complex brain science problems, new information technologies for supporting systematic brain science studies, and Brain Informatics studies based on Web intelligence research needs. These three aspects offer different ways to study traditional cognitive science, neuroscience, mental health and artificial intelligence.


Bio.:

Ning Zhong received the Ph.D. degree from the University of Tokyo. He is currently head of Knowledge Information Systems Laboratory, and a professor in Department of Life Science and Informatics at Maebashi Institute of Technology, Japan. He is also director and an adjunct professor in the International WIC Institute (WICI), a principle investigator of Brain Informatics Based Wisdom Service group at Beijing Advanced Innovation Center for Future Internet Technology. Beijing University of Technology. Dr. Zhong's present research interests include Web Intelligence (WI), Brain Informatics (BI), Data Mining, Granular Computing, and Intelligent Information Systems. Currently, he is focusing on “WI meets BI” research and brain big data computing. The synergy between WI and BI advances our ways of analyzing and understanding of data, information, knowledge, wisdom, as well as their interrelationships, organizations, and creation processes, to achieve human-level Web intelligence reality. Such interdisciplinary studies make up the field of brain informatics and its applications in brain big data computing, health studies, ICT for smart-city, brain-inspired intelligent systems among others.

Dr. Zhong is the founding editor-in-chief of Web Intelligence journal (IOS Press), the editor-in-chief of Brain Informatics journal (Springer Nature), the editor-in-chief of Brain Informatics & Health (BIH) book series (Springer Nature), and serves as associate editor/editorial board for several international journals and book series. Dr. Zhong is the co-founder and co-chair of Web Intelligence Consortium (WIC), chair of the IEEE Computational Intelligence Society Task Force on Brain Informatics, co-founder and steering committee co-chair of IEEE/WIC/ACM international conference on Web Intelligence (WI), and co-founder and steering committee co-chair of international conference on Brain Informatics (BI).


 

 

    Prof. Chengqi Zhang

Keynote title: UTS DATA Science Strategic Plan for next five years

Abstract:

In this talk, I will summarize the UTS Data Science Strategic Plan for next five years.  In this plan, the central point is about the TEAM.  This talk will include where UTS Data Science is now, what are the future targets in five years, and what to do to fill the gap and how to make it happen.  The key points are to form the teams vertically and/or horizontally.  By forming teams, the research performance will be improved dramatically.


Bio.:

Chengqi Zhang has been appointed by the University of Technology Sydney (UTS) as UTS Distinguished Professor on 24 January 2017. He has been appointed as the Executive Director UTS Data Science on 15 September 2016 to look after all researches in Data Science area cross UTS.

Prof. Zhang’s research interests mainly focus on Data Mining and its applications. He has published nearly 300 research papers, including a number of papers in the first-class international journals, such as Artificial Intelligence, IEEE and ACM Transactions. He has published seven monographs and edited 16 books. He has delivered 16 keynote/invited speeches at international conferences. He has attracted 12 Australian Research Council grants. Due to his outstanding research achievements, he had been awarded 2011 NSW Science and Engineering Awards in Engineering, Information and Communications Technology category.

Prof. Zhang is a Fellow of the Australian Computer Society (ACS) and a Senior Member of the IEEE Computer Society (IEEE). Additionally, he served in the ARC College of Experts from 2012 to 2014.

 

 

    Prof. Zhenyuan Wang

Keynote title: A Compromise for Dealing with Big Data

Abstract:

In a data set consisting of a large number of attributes, there may be some interaction among the contribution rates from various attributes towards a certain target. Usually, a suitable nonadditive set function (also called a nonadditive measure) defined on the power set of the set of all considered attributes can be adopted to describe such type of interaction and, relatively, a nonlinear integral should be used as an aggregation tool in information fusion. Such a type of nonlinear models has been applying in nonlinear regression and nonlinear classification since the nineties of the last century. In these models, the values of a nonadditive measure are regarded as unknown parameters that can be determined through a learning procedure when a necessary data set is available. However, when the number of attributes is large, the complexity of the computation in the learning procedure is very high and even is unacceptable since it is exponential with respect to the number of considered attributes. So, a compromise strategy, using 2-interactive measure as the nonadditive measure, is necessarily introduced in data mining. It can significantly reduce the computational complexity, though it has to ignore a part of interaction with degrees higher than 2.


Bio.:

Zhenyuan Wang graduated from Fudan University in 1962. He received his Ph.D. from the Department of Systems Science, State University of New York at Binghamton in 1991. He taught various mathematical courses in Hebei University for many years since 1962, supervised graduate students since 1978, and served as the Chair of the Mathematics Department there from 1985 to 1990. He was a visiting scholar, visiting professor, or research fellow in University Paris VI, Binghamton University (SUNY), the Chinese University of Hong Kong, New Mexico State University, and University of Texas at El Paso during the period from 1979 to 2008. Currently, he is a tenured full professor in the Department of Mathematics, University of Nebraska at Omaha. He received a number of honors and awards including the title of “National expert” from the Chinese National Scientific and Technological Commission in 1986 and the “Citation Classic Award from the Institute for Scientific Information (USA) in 2000. His research interests are nonadditive measures, nonlinear integrals, probability and statistics, optimization, soft computing, and data mining. He is the author or a co-author of about 150 research papers and three monographs: “Fuzzy Measure Theory” (1992), “Generalized Measure Theory” (2008), and “Nonlinear Integrals and Their Applications in Data Mining” (2010).

 

 

    斯雪明 教授

Keynote title: 区块链和网络安全

Abstract:

基于区块链与比特币技术的工作原理,分析区块链与比特币系统中存在的算法漏洞、协议漏洞、实现漏洞、使用漏洞和系统漏洞,提出区块链和比特币技术面临的网络安全挑战,结合拟态防御等网络安全新技术的发展趋势,给出应对区块链安全挑战的策略。


Bio.:

斯雪明:数学工程与先进计算国家重点实验室研究员,上海市数据科学重点实验室副主任,国家大科学工程--平方公里阵列射电望远镜项目中方专家委员会委员,享受国务院政府特殊津贴专家。中国区块链基础技术与应用创新联盟理事长,中国计算机学会区块链专家委员会负责人,中国大数据产业应用协同创新联盟副理事长。先后获国家科技进步一等奖3项,省部级科技进步一等奖4项,二等奖1项。在各类高水平学术期刊(会议)发表论文30余篇,授权和申请国家发明专利9项。主要研究方向为密码学、高性能计算体系结构、数据科学、区块链。



    朱扬勇 教授

Keynote title: 大数据的基础研究方向

Abstract:

大数据问题的关键技术挑战在于:找到隐含在低价值密度数据中的价值;在希望的时间内完成。前者需要将领域知识和数据技术结合,这种结合的理论和新型算法构成大数据的分析基础和应用基础;后者需要设计新的计算机、集群体系、计算框架、存储体系和数据管理方法,这些构成大数据的计算基础和数据基础。另外,这两个挑战都涉及数学理论,这是大数据的数学基础。本报告将系统阐述大数据的数学基础、计算基础、数据基础、分析基础和应用基础等基础研究方向。


Bio.:

朱扬勇   复旦大学计算机科学技术学院教授、学术委员会主任,上海市数据科学重点实验室主任。1989年开始从事数据领域研究,1996年开始从事数据挖掘研究,2004年开始从事数据科学研究,是国际数据科学研究的主要倡导者之一。2008年提出数据资源,指出数据资源是重要的现代战略资源,提高数据资源开发利用水平、保护国家的战略资源是增强我国综合国力和国际竞争力的必然选择2009年和熊赟教授一起,提出特异群组挖掘,发表“Mining Peculiarity Groups in Day-by-Day Behavioral Datasets”;提出数据界,发表了数据科学论文“Data Explosion, Data Nature and Dataology”,并出版了第一本数据科学专著《数据学》,探索了数据科学的概念和内涵。2010年创办“International Workshop on Dataology and Data Science”2014年和石勇教授、张成奇教授一起创办“International Conference on Data Science”2015年创办数据科学家大会。第462次香山科学会议数据科学与大数据的理论问题探索的执行主席。《大数据技术与应用丛书》主编。2014年和邬江兴院士一起提出大数据试验场2015年提出数据财政

先后主持承担国家自然科学基金、863计划、部委及上海市科研项目30多项,发表论文150多篇,出版专著和教材9本。获得上海市科技进步一、二、三等奖。

 

 

    赵运磊 教授

Keynote title: 区块链与大数据

Abstract:

 

Bio.:

赵运磊,博士,复旦大学教授。担任复旦大学解放军密码研究协同创新中心主任、中国密码学会安全协议专委会委员、中国隐私保护专委会委员、解放军密码研究协 同创新中心学术委员会委员、军用标准密码设计与分析专家组成员。主要研究兴趣:密码学理论及应用、云计算和大数据安全隐私、网络安全。在密码学与信息安全 重要国家会议和期刊发表系列论文,论文获得较大的引用,单篇论文引用超100次。多篇论文被著名密码学网站Cryptology Pointers推荐为年度代表论文或领域经典文献。