CLOUD/ICWS/SCC/MS/BigData/SERVICES 2014 Keynotes


Keynote 1: Clouds, Crowds and Provenance (06/28 Saturday, 9:50-12:00)
Keynote 2: On Mining Big Data(06/29 Sunday, 9:45-11:00)
Keynote 3: Predictive Analytics Governance in Business: A New Services Computing Frontier(06/29 Sunday, 14:30-15:45)
Keynote 4: Toward a Science of Security (and Governance) in Service Systems(06/30 Monday, 9:45-11:00)
Keynote 5: Modern Service industry in China: Crossover, Convergence, and Complex Services(07/01 Tuesday, 9:45-11:00)

Plenary Panel 1: The Next Computing Platform and Cloud Computing Standards (06/28 Saturday, 16:45-18:00)
Plenary Panel 2: Real-Time Big Data (06/29 Sunday, 16:00-17:15)
Plenary Panel 3: Services Computing: Time for the Seventh Inning Stretch (06/30 Monday, 12:00-13:15)
Plenary Panel 4: Mobile Services: Trends and Challenges (06/30 Monday, 16:00-17:30)
Plenary Panel 5: Enablement for Cloud Computing (07/01 Tuesday, 12:00-13:15)

Keynote 1: Clouds, Crowds and Provenance
Susan Davidson
University of Pennsylvania, USA

Abstract:

Harnessing the crowd to collect and/or analyze massive amounts of data (crowdsourcing) has become increasingly popular. Examples in our culture include Wikipedia, social tagging systems for images, traffic information aggregators like Waze, citizen science applications such as Galaxy Zoo, and hotel and movie ratings like TripAdvisor and IMDb. Cloud platforms are also increasingly being explored for hosting crowdsourcing systems, and for exposing the crowd as a service. In this talk, I give an overview of the challenges inherent in providing declarative, database-style platforms for supporting crowdsourcing. I then discuss how to go one step further and enable users to posed general questions to mine the crowd and to receive concise, relevant answers that represent frequent significant data patterns. Finally, I discuss the importance of capturing provenance for crowdsourcing applications, and the inherent challenges due to the massive size of data and complex analytics.

About the Speaker:
Dr. Susan Davidson

Professor Susan B. Davidson is the Weiss Professor of Computer and information Science (CIS) at the University of Pennsylvania (Penn). She received her B.A. degree in Mathematics from Cornell University in 1978, her Ph.D. degree in Electrical Engineering and Computer Science from Princeton University in 1982, and joined Penn in 1982. Dr. Davidson was a founding co-Director of the Penn Center for Bioinformatics as well as the regional Greater Philadelphia Bioinformatics Alliance, served as the Deputy Dean of the School of Engineering and Applied Science, is the founding Faculty Director of Advancing Women in Engineering at Penn, and recently stepped down as Chair of CIS. She is an ACM Fellow, a Fulbright scholar, and serves on the Board of Directors of the Computing Research Association and the Executive Committee of the Computing Community Constorium. Dr. Davidson's research interests include database systems, database integration, distributed systems, bioinformatics, scientific workflow systems, provenance, and crowd data-sourcing.



Keynote 2: On Mining Big Data
Philip S. Yu
University of Illinois at Chicago, USA

Abstract: The problem of big data has become increasingly important in recent years. On the one hand, the big data is an asset that potentially can offer tremendous value or reward to the data owner. On the other hand, it poses tremendous challenges to mine the value out of the big data. The very nature of the big data poses challenges not only due to its volume, and velocity of being generated, but also its variety. Here variety means the data can be collected from various sources with have different formats. One of the most critical big data applications is mining social networks. As social networks become increasingly popular, not only the scale of the networks grows rapidly, but also the complexity of the networks increases over time. In this talk, we will discuss these big data issues and approaches to address them using social networks as an example.

About the Speaker:
Dr. Philip S. Yu

Professor Philip S. Yu is a Distinguished Professor and the Wexler Chair in Information Technology at the Department of Computer Science, University of Illinois at Chicago. Before joining UIC, he was at the IBM Watson Research Center, where he built a world-renowned data mining and database department. He is a Fellow of ACM and IEEE. Dr. Yu has been making tremendous, and globally recognized contributions to the principles of mining big data. He is the recipient of IEEE Computer Society’s 2013 Technical Achievement Award for “pioneering and fundamentally innovative contributions to the scalable indexing, querying, searching, mining and anonymization of big data”. Dr. Yu has published more than 800 papers in refereed journals and conferences, cited by more than 52,000 times with an H-index of 109. 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 the IEEE Conference on Data Mining and ACM Conference on Information and Knowledge Management, and was a member of the IEEE Data Engineering steering committee. He was the Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering (2001-2004). He received a Research Contributions Award from IEEE Intl. Conference on Data Mining (ICDM) in 2003, the ICDM 2013 10-year Highest-Impact Paper Award, and the EDBT Test of Time Award (2014). Dr. Yu received his PhD from Stanford University.



Keynote 3: Predictive Analytics Governance in Business: A New Services Computing Frontier
Michael Goul
Arizona State University, USA

Abstract: The proliferation of deployed predictive analytics - all across business service landscapes - will serve to challenge earlier conceptualizations of services computing and business process management/integration. For example, monitoring for predictive model decay in embedded ‘Internet of things’ software services represents a new governance challenge and opportunity. Big data acquisition and integration for purposes of testing for predictive model decay will require new insights for SOA platforms. To address this new frontier for services computing, we will generalize a business perspective to the deployment of predictive analytics, discuss strategies for predictive analytics governance and demonstrate the pitfalls of failing to implement appropriate governance mechanisms. Reconciling data scientists’ current predictive analytics discovery methodologies with what it takes to deploy analytics into modern IT service systems represents a new corporate balancing act. The hand-offs between data scientists and services computing developers need to be examined in light of new compliance realities that impact the services lifecycle. Abstracting from current best practices in companies like UBS, eHarmony, Linked-in, eBay, American Express and others provides a fresh perspective to predictive analytics governance. Challenges to be faced are ripe for research innovations by the services computing community.

About the Speaker:
Dr. Michael Goul

Professor Michael Goul is Professor and Chair of the Department of Information Systems, W.P. Carey School of Business, Arizona State University. His research bridges services computing and analytics. He was Co-PI on grants from Intel and American Express at the intersection of these areas, and he recently authored a case on eBay’s approach to experimentation that relies on a multi-tenant, self-service data warehouse infrastructure. His work in services computing led to a one-year appointment as a Distinguished University Scholar at the Clinton School of Public Service. Goul is also on the research faculty of the W.P. Carey School’s Center for Services Leadership.



Keynote 4: Toward a Science of Security (and Governance) in Service Systems
Munindar Singh
North Carolina State University, USA

Abstract:

Despite sustained research effort in security, current security practice conveys a decidedly ad hoc flavor --- find a bug; patch it; find the next bug; and so on. This methodology is sometimes termed "engineering", using the term in the narrow sense of developing solutions to specific problems. The past few years have seen a growing push to develop a Science of Security (SoS), viewed as a systematic body of knowledge with strong theoretical and empirical underpinnings that inform the engineering of secure information systems.

I introduce SoS, briefly describing its key elements. I then motivate some of the foundational challenges of SoS from the standpoint of systems of autonomous participants, such as cross-organizational service systems. I describe how security is an element of the governance of such systems. I introduce an approach for governance based on a new formulation of norms and accountability, showing how it addresses the challenges of security pertaining to secure collaboration and would facilitate the development of flexible and secure cross-organizational service engagements.

About the Speaker:
Dr. Munindar Singh

Professor Munindar Singh is a professor in the Department of Computer Science at North Carolina State University. Munindar's research interests include service-oriented computing, security, and social computing. He addresses the challenges of trust, norms, requirements modeling, service ecosystems, and business processes and protocols in large-scale open environments.

Munindar is the editor-in-chief of ACM Transactions on Internet Technology. From 1999 to 2002, he was the editor-in-chief of IEEE Internet Computing. His current editorial service includes IEEE Internet Computing, Journal of Autonomous Agents and Multiagent Systems, IEEE Transactions on Services Computing, ACM Transactions on Intelligent Systems and Technology, the Journal of Artificial Intelligence Research, and the Journal of Trust Management. Munindar served on the founding board of directors of IFAAMAS, the International Foundation for Autonomous Agents and MultiAgent Systems.

Munindar is a Fellow of the IEEE. His research has been recognized with awards and sponsorship by (alphabetically) Army Research Lab, Army Research Office, Cisco Systems, Consortium for Ocean Leadership, DARPA, Ericsson, IBM, Intel, National Science Foundation, National Security Agency, and Xerox. Nineteen students have received PhD degrees and 27 students MS degrees under Munindar's direction.



Keynote 5: Modern Service industry in China: Crossover, Convergence, and Complex Services
Zhaohui Wu
Zhejiang University, China

Abstract:

Modern Service industry is not only becoming a leading and pillar industry for economic development, but also an important indicator of measuring the degree of production socialization and market economic development. As a matter of course, Service Oriented Computing, as the academic foundation of Modern Service industry, attracts lots of attention in the last decade. However, the big data and the development of emerging computing paradigms such as Cloud Computing, internet-Of-Things, and Mobile internet have made Service Oriented Computing more and more complex. in this talk, we will give the concept and overview of the development trend of Modern Service industry in the world, especially the development of Modern Service industry in China. Moreover, we give the concept and the definition of complex service, which has the characteristics of ‘3C’, i.e., convergence, and complex. Further, a technique architecture for complex service computing is provided and some case studies of complex services are provided.

About the Speaker:
Dr. Zhaohui Wu

Professor Zhaohui Wu is a Qiushi Professor of Zhejiang University and the director of the institute of Computer System and Engineering. He is the committee chair of National S&T innovation Plan on Modern Service industry (MSCI) and the Distinguished Young Scholar of China National Science Foundation (NSFC). He is the director of MOE’s Research Center of intelligence Science and Technique and the head of MOE’s R&D Center of High-Performance Embedded Computing. He is the Standing Member and a fellow of the China Computer Federation (CCF). His research interests include Service Computing and intelligent systems. Dr. Wu has authored 9 books, more than 200 refereed papers and over 100 invention patents, as well as 2 national S&T progress prize II. He is the founding editor-in-chief of Elsevier’s Big Data Research Journal, the associated editor of Chinese Journal of information on Traditional Chinese Medicine and the founder of three International conferences (ICESS, CPSCom and MSCI).