SCF 2017 Keynotes

Keynote 1: Parallel Services in Fluid Computing: An ACP based Approach for Computational and Smart Services

Speaker:

  • Fei-Yue Wang, Ph.D. Chinese Academy of Sciences, China

Abstract:

The World Wide Web Consortium (W3C) defines a web service as “a software system designed to support interoperable machine-to-machine interaction over a network”. Fluid computing unifies cloud, fog, and mist computing, and inevitably current cloud based computing infrastructure will evolve to this new computing morphology. How web services are re-shaped by fluid computing, and how to effective manage and control web services to adapt to the new computing morphology, are among the next major challenges in web-based service systems. The presentation discusses the utilization of parallel system methodology and ACP approach to respond to these challenges, and aiming for building the next generation highly efficient and capability-enhanced web service systems.

About the Speaker:

Feiyue Wang Fei-Yue Wang received his Ph.D. in Computer and Systems Engineering from Rensselaer Polytechnic Institute, Troy, New York in 1990. He joined the University of Arizona in 1990 and became a Professor and Director of the Robotics and Automation Lab (RAL) and Program in Advanced Research for Complex Systems (PARCS). In 1999, he founded the Intelligent Control and Systems Engineering Center at the Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, China, under the support of the Outstanding Overseas Chinese Talents Program from the State Planning Council and ''100Talent Program'' from CAS, and in 2002, was appointed as the Director of the Key Lab of Complex Systems and Intelligence Science, CAS. From 2006 to 2010, he was Vice President for Research, Education, and Academic Exchanges at the Institute of Automation, CAS. In 2011, he became the State Specially Appointed Expert and the Director of the State Key Laboratory of Management and Control for Complex Systems. Dr. Wang's current research focuses on methods and applications for parallel systems, social computing, and knowledge automation. He was the Founding Editor-in-Chief of the International Journal of Intelligent Control and Systems (1995-2000), Founding EiC of IEEE ITS Magazine (2006-2007), EiC of IEEE Intelligent Systems (2009-2012), and EiC of IEEE Transactions on ITS (2009-2016). Currently he is EiC of IEEE Transactions on Computational Social Systems, Founding EiC of IEEE/CAA Journal of Automatica Sinica, and Chinese Journal of Command and Control. Since 1997, he has served as General or Program Chair of more than 20 IEEE, INFORMS, ACM, and ASME conferences. He was the President of IEEE ITS Society (2005-2007), Chinese Association for Science and Technology (CAST, USA) in 2005, the American Zhu Kezhen Education Foundation (2007-2008), and the Vice President of the ACM China Council (2010-2011). Since 2008, he has been the Vice President and Secretary General of Chinese Association of Automation. Dr. Wang has been elected as Fellow of IEEE, INCOSE, IFAC, ASME, and AAAS. In 2007, he received the National Prize in Natural Sciences of China and was awarded the Outstanding Scientist by ACM for his research contributions in intelligent control and social computing. He received IEEE ITS Outstanding Application and Research Awards in 2009, 2011 and 2015, and IEEE SMC Norbert Wiener Award in 2014.

Keynote 2: From Self-Learning to Knowledge Discovery

Speaker:

  • Aidong Zhang, Ph.D. NSF, US

Abstract:

With the growth of world wide web and large-scale digitization of documents, we are overwhelmed with massive information, formally through publication of various scientific journals or informally through internet. As an example, consider MEDLINE, a premier bibliographic database in life sciences, with currently more than 23 million references from approximately 5,600 worldwide journals. As a consequence, Literature Based Discovery (LBD) has become a sub-field of Text Mining that leverages these published articles to formulate hypotheses. In this talk, I will discuss how a self-learning based framework for knowledge discovery can be designed to mine hidden associations between non-interacting scientific concepts by rationally connecting independent nuggets of published literature. The self-learning process can model the evolutionary behavior of concepts to uncover latent associations between text concepts, which allows us to learn the evolutionary trajectories of text terms and detect informative terms in a completely unsupervised manner. Hence, meaningful hypotheses can be efficiently generated without prior knowledge. I will also discuss how this self-learning framework can be extended to include social media and Internet forums. With the capability to discern reliable information from various sources, this self-learning framework provides a platform for combining heterogeneous sources and intelligently learning new knowledge with no user intervention.

About the Speaker:

Zhang Dr. Aidong Zhang is a SUNY Distinguished Professor of Computer Science and Engineering at the State University of New York (SUNY) at Buffalo where she served as Department Chair from 2009 to 2015. She is currently on leave and serving as Program Director in the Information & Intelligent Systems Division of the Directorate for Computer & Information Science & Engineering, National Science Foundation. Her research interests include data analytics/data science, bioinformatics, and health informatics, and she has authored over 300 research publications in these areas. Dr. Zhang currently serves as the Editor-in-Chief of the IEEE Transactions on Computational Biology and Bioinformatics (TCBB). She served as the founding Chair of ACM Special Interest Group on Bioinformatics, Computational Biology and Biomedical Informatics during 2011-2015 and is currently Chair of its advisory board. She is also the founding and steering chair of ACM international conference on Bioinformatics, Computational Biology and Health Informatics. She has served as editor for several other journal editorial boards, and has also chaired or served on numerous program committees of international conferences and workshops. Dr. Zhang is an IEEE Fellow.

Keynote 3: Computing in the Continuum: Harnessing a Pervasive Data Ecosystem

Speaker:

  • Manish Parashar, Rutgers University, US

Abstract:

The exponential growth of digital data sources enabled by the IoT, coupled with the ubiquity of non-trivial computational power, at the edges, in the core and in-between, for processing this data have the potential for fundamentally transforming our ability to understand and manage our lives and our environment. However, despite tremendous advances in technology this vision remains largely unrealized -- while our capacity for generating data is expanding dramatically, our ability for managing, manipulating and analyzing this data, for transforming it into knowledge and understanding in a timely manner, and for integrating it with practice has not kept pace. In this talk I will explore computing in the continuum – a paradigm that opportunistically leverages loosely connected resources and services to process data in-situ and in-transit, to extract timely insights that are actionable. Using examples from our work as part of the CometCloud project, I will present research challenges and some initial solutions towards realizing this paradigm.

About the Speaker:

Manish Manish Parashar is Distinguished Professor of Computer Science at Rutgers University. He is also the founding Director of the Rutgers Discovery Informatics Institute (RDI2). His research interests are in the broad areas of Parallel and Distributed Computing and Computational and Data-Enabled Science and Engineering. Manish is founding chair of the IEEE Technical Consortium on High Performance Computing (TCHPC), and serves on the editorial boards and organizing committees of several journals and international conferences and workshops. He has over 350 publication, has deployed software systems that are widely used, and has received awards for his research and leadership. Manish is Fellow of AAAS, Fellow of IEEE/IEEE Computer Society and ACM Distinguished Scientist. For more information please visit http://parashar.rutgers.edu/.

Keynote 4: Big Data Software: What’s Next?

Speaker:

  • Michael J. Franklin, University of Chicago, USA

Abstract:

The Big Data revolution has been enabled in part by a wealth of innovation in software platforms for data storage, analytics, and machine learning. The first wave of Big Data platforms such as Hadoop and Spark focused on scalability, fault-tolerance and performance. As these and other systems increasingly become part of the mainstream, the next set of challenges are becoming clearer. Requirements for performance are changing as workloads evolve to include techniques such as hardware-accelerated deep learning. But more fundamentally, other issues are moving to the forefront. These include ease of use for a wide range of users, security, concerns about privacy and potential bias in results, and the perennial problem of data integration from heterogeneous sources. In this talk, I will give a quick overview of how we got here, with an emphasis on the development of the Apache Spark system. I will then focus on these emerging issues and approaches towards tackling them.

About the Speaker:

Michael Michael J. Franklin is the Liew Family Chair of Computer Science and Sr. Advisor to the Provost for Computation and Data at the University of Chicago where his research focuses on database systems, data analytics, data management and distributed computing systems. Franklin previously was the Thomas M. Siebel Professor and Chair of the Computer Science Division of the EECS Department at the University of California, Berkeley. He co-founded and directed Berkeley’s Algorithms, Machines and People Laboratory (AMPLab), which created industry-changing open source Big Data software such as Apache Spark and BDAS, the Berkeley Data Analytics Stack. At Berkeley he also served as an executive committee member for the Berkeley Institute for Data Science. He currently serves as a Board Member of the Computing Research Association and on the NSF CISE Advisory Committee. Franklin is an ACM Fellow, a two-time recipient of the ACM SIGMOD “Test of Time” award and received the Outstanding Advisor award from Berkeley’s Computer Science Graduate Student Association.

Keynote 5: Business and Social Impact of Services based on IOTs and AI

Speaker:

  • Hemant Jain,University of Tennessee Chattanooga, USA

Abstract:

Offering of new and innovative services based on data collected by IOTs and the resulting AI applications are expected to have profound impact on businesses and society in medium term. This talk will focus on services based on IOT and AI technologies that are on the horizon, how it may impact various businesses and the society. The talk will conclude with identification of issues the researchers need to consider as they develop these technologies.

About the Speaker:

Hemant Hemant Jain is W. Max Finely Chair in American Business: Data Analytics, in College of Business at University of Tennessee Chattanooga. Before this he was Professor of Information Technology Management at University of Wisconsin Milwaukee. He is internationally acclaimed for his pioneering work on Effectiveness of Presentation of Product Information in E-Business Systems. Results of his work is widely adopted in design of e-commerce sites. Some of the product presentation features such as three dimensional rotating images of product, ability to chat with sales person in real-time proved effective by Prof. Jain are common in most e-commerce sites today. His paper on this topic published in European Journal of Information Systems was awarded Operations Research Society's Stafford Beer Medal for the best paper published in the European Journal of Information Systems in 2002. He is also internationally known for his work in component based system development and service oriented architecture. is work has appeared in leading journals including Information Systems Research, MIS Quarterly, IEEE Transactions on Software Engineering, Journal of MIS, IEEE Transactions on Systems Man and Cybernetics, Naval Research Quarterly, Decision Sciences, Decision Support Systems, Communications of ACM, and Information & Management. Dr. Jain is on the advising board of IEEE Transactions on Services Computing. He served as Associate Editor-in-Chief of IEEE Transactions on Services Computing and as Associate Editor of Journal of AIS. Recently he served as Program Chair, of IEEE International Conference on Big Data and is serving as Program Chair of IOT services conference. He served as Program co-chair of DESRIST 2011.

Contact Information

If you have any questions or queries on ICWS 2017, please send email to ICWS AT ServicesSociety.org.

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