12th International Conference on Enterprise Information Systems
8 - 12 June, 2010            Funchal, Madeira - Portugal

Keynote lectures are plenary sessions which are scheduled for taking about 45 minutes + 10 minutes for questions.


Michel Chein, LIRMM, University of Montpellier 2, France
           Title: Graph-Based Knowledge Representation and Reasoning

David L. Olson, University of Nebraska, U.S.A.
           Title: Contemporary Trends in Enterprise Information Systems

Anind K. Dey, Carnegie Mellon University, U.S.A.
           Title: Intelligence and Context-aware Applications

Runtong Zhang, Beijing Jiaotong University, China
           Title: Wireless Sensor Networks (WSN) and Internet of Things (IOT)

Robert P. W. Duin, TU Delft, The Netherlands
           Title: Pattern Recognition as a Human Centered non-Euclidean Problem

Keynote Lecture 1
Graph-Based Knowledge Representation and Reasoning
Michel Chein
Michel Chein
LIRMM, University of Montpellier 2

Brief Bio
Michel Chein is Emeritus Professor of Computer Science at the University of Montpellier 2 and researcher in Knowledge Representation at LIRMM - Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier.
Before working at the University of Montpellier, Prof. Chein was a researcher at CNRS and a Professor at the Universities of Le Mans and Paris 6. He founded and directed the Centre de Recherche en Informatique de Montpellier and the École Doctorale "Information, Structures, Systèmes" at the University of Montepellier, and was an Associate Editor of the "Revue d'Intelligence Artificielle".
Prof. Chein has been involved in numerous conferences, as both a member or director of several committees and invited speaker. With more than 70 publications in graph theory, order theory, algorithmics, artificial intelligence and information retrieval, Prof. Chein co-authored the recently published book: "Graph-based Knowledge Representation".

The model presented in this talk is a computational model. It aims at representing knowledge by computational objects and at reasoning with the represented knowledge, i.e., at processing them by algorithms (philosophical or psychological aspects of knowledge will not be discussed).
We first present the main properties a knowledge representation formalism should have and briefly survey graph or graphical models (in Computer Science).
Then, we detail the model itself , which is graph-based in the following sense: knowledge is represented by labeled graphs and reasoning mechanisms are based on graph operations.
The third part is devoted to the relationships of this model with logics and other computational models, especially the relational data base model and RDF/S.
Finally, some tools and applications are mentioned.

Keynote Lecture 2
Contemporary Trends in Enterprise Information Systems
David L. Olson
David L. Olson
University of Nebraska

Brief Bio
David L. Olson is the James & H.K. Stuart Professor in MIS and Chancellor’s Professor at the University of Nebraska. He has published research in over 100 refereed journal articles, primarily on the topic of multiple objective decision-making and information technology. He teaches in the management information systems, management science, and operations management areas. He has authored 17 books, to include Decision Aids for Selection Problems, Introduction to Information Systems Project Management, and Managerial Issues of Enterprise Resource Planning Systems as well as co-authored the books Introduction to Business Data Mining, Enterprise Risk Management, Advanced Data Mining Techniques, New Frontiers in Enterprise Risk Management, Enterprise Information Systems, and Enterprise Risk Management Models. He is associate editor of Service Business and co-editor in chief of International Journal of Services Sciences. He has made over 100 presentations at international and national conferences on research topics. He is a member of the Association for Information Systems, the Decision Sciences Institute, the Institute for Operations Research and Management Sciences, and the Multiple Criteria Decision Making Society. He was named Best Enterprise Information Systems Educator by IFIP. He is a Fellow of the Decision Sciences Institute.

Enterprise resource planning (ERP) became a very important form of organizational information system (EIS) in the 1990s. This century has seen an evolution into enterprise information systems, to include expansion of the software to include features such as customer relationship management and supply chain management support. This presentation reviews the evolution of EIS, and associated paradigm shifts. The features expected of the next generation of EIS software are given. The presentation also discusses research in EIS, to include a series of surveys concerning motivations and expectations of users. Vendors are moving to improve their systems, leading to a greater focus in this century on upgrading old ERP systems. Research examining the impact of upgrades is reviewed. Emerging issue areas of ERP risk management and open source opportunities are discussed.

Keynote Lecture 3
Intelligence and Context-aware Applications
Anind K. Dey
Anind K. Dey
Carnegie Mellon University

Brief Bio
Anind K. Dey is an Associate Professor in the Human-Computer Interaction Institute at Carnegie Mellon University. His interests lie at the intersection of human-computer interaction, machine learning and ubiquitous computing. He has spent the last decade developing techniques for building context-aware applications, and for improving the usability of such applications. Anind is the author of over 100 articles in the area of ubiquitous computing, has served as the Program Chair for several conferences on ubiquitous computing and serves on the editorial board for IEEE Pervasive Computing and the Personal and Ubiquitous Computing Journal. Before joining Carnegie Mellon University, Anind was a Senior Researcher at Intel Research Berkeley and an Adjunct Assistant Professor at the University of California-Berkeley. He holds a PhD and a Masters degree in Computer Science, as well as a Masters degree in Aerospace Engineering, all from Georgia Tech, and a Bachelors of Computer Engineering from Simon Fraser University.

The world is changing faster than we can predict. The concept of ubiquitous computing that was first voiced 30 years ago is now here, with the introduction of location-based services (LBS) on commodity mobile phones. To conduct research in ubiquitous computing, we no longer have to provide special purpose devices to people - they carry them already. However, despite the widespread use of simple context-aware services such as LBS, there is still much room for improvement.
Context-aware systems attempt to infer human intent and adapt to that intent, however, at best, they can only approximate human intent. That approximation results in all sorts of usability problems. In this talk, I will discuss the usability problems that result from trying to build sophisticated, real-world context-aware applications that attempt to infer human intent. I will show examples of systems that have succeeded and failed, and discuss the role of machine intelligence in designing good context-aware systems. Finally, I will discuss new types of interfaces, algorithms and support that such applications need to have to support real human activities.

Keynote Lecture 4
Wireless Sensor Networks (WSN) and Internet of Things (IOT)
Runtong Zhang
Runtong Zhang
Beijing Jiaotong University

Brief Bio
Dr. Runtong Zhang is presently a professor and the head of the Department of Information Management, in the School of Economics and Management at the Beijing Jiaotong University in Beijing, China. He has previously worked with the Swedish Institute of Computer Science in Stockholm, Sweden, as a permanent Senior Researcher, and the Nokia (China) R&D Center in Beijing, China, as a Senior Research Consultant. Dr. Zhang has paid academic visits to universities or institutes in more twenty countries, such as USA, Japan, UK and Australia. He was a general co-chair of 2008 IEEE International Conference on Service Operations, Logistics and Informatics (IEEE/SOLI’2008) held in Beijing. He has also been serving as a conference chair or IPC member for numerous international academic conferences, and editor or guest editor for several international journals.

Dr. Zhang’s research experiences cover the areas of logistics and information technologies, mobile and distributed computing, operations research, and electronic commerce. He has published more than 160 papers in refereed journals and conferences in these areas. He is also an author or co-author of 25 books, a principal investigator of over 40 research projects, and a holder of three international patents (with Nokia) in the area of next generation Internet. The course of “Electronic Commerce” he taught was entitled the “Best Course in China in 2004” by the Educational Ministry of China.

Together with the rapid development of the three technical areas of the microelectronics and micro-systems (MEMS), wireless communications, and signal processing in recent years, a new field of study has globally become the focus of research. It is the so called wireless sensor networks (WSN) which can be constructed by a lot of miniature wireless sensors with communication capacity. Internet of Things (IOT) is a huge information network, structured by using RFID, WSN and wireless data communications technologies, which makes everything in the world covered and connected. IOT is the third wave of the world's information industry after the dissemination of computers and the Internet. It is estimated that the output of IOT industry, whenever it is mature, will be at least 30 times as much as that of the Internet industry. This talk will cover the vigorous development of wireless sensor networks WSN and IOT, hot technology spots of WSN, RFID and IOT, and their applications. Attention of these topics is specially paid on those in China.

Keynote Lecture 5
Pattern Recognition as a Human Centered non-Euclidean Problem
Robert Duin
Robert P. W. Duin
TU Delft
The Netherlands

Brief Bio
Robert P.W. Duin received in 1978 the Ph.D. degree in applied physics from Delft University of Technology, Delft, The Netherlands, for a thesis on statistical pattern recognition. He is currently an Associate Professor in the Faculty of Electrical Engineering, Mathematics and Computer Science of the same university.
During 1980-1990, he studied and developed hardware architectures and software configurations for interactive image analysis. After that he became involved with pattern recognition by neural networks. His current research interests are in the design, evaluation, and application of algorithms that learn from examples, which includes neural network classifiers, support vector machines, classifier combining strategies, and one-class classifiers.
Especially complexity issues and the learning behavior of trainable systems receive much interest. Recently, he started to investigate alternative object representations for classification and he thereby became interested in dissimilarity-based pattern recognition, trainable similarities, and the handling of non-Euclidean data.
Dr. Duin is an associated editor of Pattern Recognition Letters and a past-associate editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence. He is a Fellow of the International Association for Pattern Recognition (IAPR). In August 2006 he was the recipient of the Pierre Devijver Award for his contributions to statistical pattern recognition.

Regularities in the world are human defined. Patterns in the observed phenomena are there because we define and recognize them as such. Automatic pattern recognition has thereby to bridge the gap between human judgment and measurements made by artificial sensors. A good, well performing pattern recognition system is adapted to what we experience as similar and as dissimilar. A natural way to design such a system is thereby based on the pair wise comparison of the objects for which patterns should be recognized. This results in the dissimilarity representation for pattern recognition. An analysis of such representations optimised for performance shows that they tend to be non-Euclidean. The Euclidean vector spaces, traditionally used in pattern recognition and machine learning are for these cases suboptimal. The causes and consequences of the use of non-Euclidean representations will be discussed.

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