Tutorials

Tutorial I:

The Future of Collaboration: Ten Trends That Will Radically Change the Collaboration Environment We Know Today!

David Coleman

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Notes

Tutorial II:

Collaborative Knowledge Acquisition from Semantically Disparate, Distributed Data Sources

Vasant Honavar and Doina Caragea

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Notes

Tutorial III:

Cross-Cultural User-Experience Design

Aaron Marcus

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TUTORIAL I The Future of Collaboration: Ten Trends That Will Radically Change the Collaboration Environment We Know Today! David Coleman Founder and Managing Director Collaborative Strategies San Francisco, California USA ABSTRACT There are a number of factors (trends) coming together over the last few years to form a "perfect wave" that will make collaboration in the near future look very different than it does today. This presentation will detail each of these trends, presenting the data points from Collaborative Strategies research as well as discussion on each trend. The tutorial will trace the rise of electronic collaboration over the last 15 years and look at what the current trends and drivers are for collaboration today. To complete this presentation we will detail some of Collaborative Strategies predictions for the future of collaboration (both synchronous and asynchronous). This tutorial will also feature a number of interactive exercises to gauge how collaborative the attendees and their organizations are. The presenter then will look at first, second, and third order effects of these technologies on society and how this will change our lives in the near and far future. REQUIRED BACKGROUND AND INTENDED AUDIENCE This tutorial is geared for academics, IT managers and engineers interested in collaboration as well as executives from collaboration software vendors. TUTORIAL DURATION Two Hours. METHOD OF PRESENTATION Data projector and a laptop. Tutorial notes will be made available as a PDF of PPT slides. PRESENTER David Coleman Founder and Managing Director Collaborative Strategies San Francisco, CA, USA Email: davidc@collaborate.com INSTRUCTOR'S BIOGRAPHY David Coleman has been involved with groupware, collaborative technologies, and knowledge management (KM) since 1989. He is a frequent public speaker, an industry analyst, and author of books and magazine articles on electronic collaboration and knowledge management. He has been the conference chairman for both groupware and KM conferences. Based in San Francisco, Mr. Coleman leads one of the world's foremost IT analyst and consulting firm focused on electronic collaboration. He is currently the editor of the CS "Inside Collaboration" newsletter and writes the Guru's corner column for that publication. As a thought leader he also frequently writes in the ˇ°Collaboration Blogˇ± www.collaborate.com and does podcasts on collaboration. In the past, he has helped to start two other computer industry publications, written two books on Groupware (published by Prentice Hall) and worked as a VP of Marketing at a Natural Language start-up. He was also formerly a product line manager at Oracle Corporation and helped to start the UNIX group there, growing his segment from $0- $20M in revenues in just one year. He has worked with many start-ups and most of the major vendors in the collaboration space as well as a wide variety of end-user organizations. He can be reached directly at: davidc@collaborate.com, or 415-282-9197. Back to top TUTORIAL II Collaborative Knowledge Acquisition from Semantically Disparate, Distributed Data Sources Vasant Honavar and Doina Caragea Artificial Intelligence Research Laboratory Department of Computer Science Center for Computational Intelligence, Learning, and Discovery Iowa State University Ames, Iowa 50011 USA honavar@cs.iastate.edu, dcaragea@cs.iastate.edu www.cild.iastate.edu TUTORIAL DESCRIPTION Development of high throughput data acquisition technologies, together with advances in computing, and communications have resulted in an explosive growth in the number, size, and diversity of potentially useful information sources. This has created unprecedented opportunities for data-driven knowledge acquisition and decision-making in a number of emerging increasingly data-rich application domains, such as bioinformatics, environmental informatics, medical informatics, enterprise informatics, security informatics (among others). However, the massive size, semantic heterogeneity, autonomy, and distributed nature of the data repositories present significant hurdles in acquiring useful knowledge from the available data. Against this background, there is an urgent need for software systems for collaborative knowledge acquisition from autonomous, semantically heterogeneous, distributed information sources. This tutorial will: Introduce some of the specific challenges in the design of software systems for collaborative knowledge acquisition from autonomous, semantically heterogeneous, distributed information sources. Present a sufficient statistics based general framework for learning from such sources. Describe how this framework can be used to transform standard learning algorithms into algorithms for knowledge acquisition from distributed data and show that the resulting algorithms offer rigorous performance guarantees (relative to their centralized, single agent, or batch counterparts that assume centralized access to the entire data set). Introduce ontology-extended data sources (OEDS) to facilitate collaborative analysis of semantically heterogeneous information sources. OEDS make explicit the structure (schema) and semantics (content) of the data sources, as well as the query answering capabilities of these sources. Introduce a framework for specifying semantic correspondences that reconcile the semantic differences between a user view and the individual information sources in some important special cases (e.g., partial order ontologies) that are commonly encountered in practice. Describe how the sufficient statistics based framework for learning from distributed data can be extended to yield theoretically well-founded algorithms for learning from semantically heterogeneous, autonomous information sources. Point out some statistical problems that arise when learning from data in this setting, e.g., problems caused by the differences in the levels of abstraction used by autonomous information sources to describe the objects of interest. Conclude with some open problems and promising avenues for further research. TARGET AUDIENCE The tutorial should be accessible to beginners, but should also be of interest to advanced practitioners who are unfamiliar with the specific topics to be covered in the course. TUTORIAL DURATION The tutorial material will be organized into 2 modules of an hour each (for a 2 hour tutorial). INSTRUCTOR BIOGRAPHIES Dr. Vasant Honavar received his Ph.D. in Computer Science from the University of Wisconsin, Madison in 1990. He is currently a full professor of Computer Science at Iowa State University (ISU). Honavar directs the Center for Computational Intelligence, Learning and Discovery (www.cild.iastate.edu), which he founded in 2005 and the Artificial Intelligence Research Laboratory (which he founded in 1990) at ISU. Honavar's research and teaching interests include Artificial Intelligence, Machine Learning, Bioinformatics, Computational Molecular Biology, Collaborative Information Systems, Semantic Web, Environmental Informatics, Security Informatics, Social Informatics, Neural Computation, Systems Biology, Data Mining, Knowledge Discovery and Visualization. Honavar has published over 150 research articles in refereed journals, conferences and books, and has co-edited 6 books. Honavar is a co-chair of the 2006 AAAI Fall symposium on Semantic Web for Collaborative Knowledge Acquisition. Dr. Doina Caragea received her Ph.D. in Computer Science, specializing in artificial intelligence, in 2004 from Iowa State University, where she worked with Professor Vasant Honavar. Dr. Caragea has published more than 12 refereed conference papers and journal articles. Dr. Caragea is currently a postdoctoral research associate in the Iowa State University Center for Computational Intelligence, Learning, and Discovery. Her research interests include artificial intelligence, machine learning, data mining and knowledge discovery, statistical query answering, visual data mining, ontologies, information integration, semantic web, computational biology and bioinformatics, and collaborative information systems. She has published several papers in refereed conferences and journals on these topics. Caragea is a co-organizer of the 2006 AAAI Fall Symposium on Semantic Web for Collaborative Knowledge Acquisition. REFERENCES Caragea, D., Zhang, J., Bao, J., Pathak, J., and Honavar, V. (2005). Algorithms and Software for Collaborative Discovery from Autonomous, Semantically Heterogeneous, Distributed, Information Sources. In: Proceedings of the Conference on Algorithmic Learning Theory. Lecture Notes in Computer Science. Vol. 3734. Berlin: Springer-Verlag. pp. 13-44. Honavar, V. Caragea, D., Zhang, and Bao, J. (2006). Algorithms and Software for Collaborative Discovery from Autonomous, Semantically Heterogeneous, Distributed Information Sources. (2006). Research Monograph. To appear. Zhang, J., Kang, D-K., Silvescu, A. and Honavar, V. (2005). Learning Compact and Accurate Naive Bayes Classifiers from Attribute Value Taxonomies and Partially Specified Data. Knowledge and Information Systems. DOI 10.1007/s10115-005-0211-z Caragea, D., Silvescu, A., and Honavar, V. (2004). A Framework for Learning from Distributed Data Using Sufficient Statistics and its Application to Learning Decision Trees. International Journal of Hybrid Intelligent Systems. Vol 1. pp. 80-89, 2004 Caragea, D., Silvescu, A., Pathak, J., Bao, J., Andorf, C., Dobbs, D., and Honavar, V. (2005) Information Integration and Knowledge Acquisition from Semantically Heterogeneous Biological Data Sources. Data Integration in Life Sciences (DILS 2005), San Diego, Berlin: Lecture Notes in Computer Science. Berlin: Springer-Verlag. Vol. 3615 pp. 175-190. Caragea, D., Pathak, J., and Honavar, V. (2004). Learning Classifiers from Semantically Heterogeneous Data. In: Proceedings of the International Conference on Ontologies, Databases, and Applications of Semantics (ODBASE 2004), Agia Napa, Cyprus, 2004. Zhang, J. and Honavar, V. (2004). AVT-NBL - An Algorithm for Learning Compact and Accurate Naive Bayes Classifiers from Attribute Value Taxonomies and Data. In: Proceedings of the IEEE International Conference on Data Mining. Caragea, D., Silvescu, A., and Honavar, V. (2003) Decision Tree Induction from Distributed, Heterogeneous, Autonomous Data Sources. In: Proceedings of the Conference on Intelligent Systems Design and Applications (ISDA 2003). Springer Verlag. Back to top TUTORIAL III Cross-Cultural User-Experience Design Aaron Marcus President Aaron Marcus and Associates, Inc. Berkeley, California USA http://www.amanda.com/ TUTORIAL DESCRIPTION User-experience design is at the top of concerns for product/service user-interface development, especially for global deployment. How do cultural differences affect that experience? That question cuts across all platforms (e.g., Web, client-server PCs, mobile, appliances), applications (e.g., productivity, entertainment, commerce), user communities (e.g., professional, consumers), and markets (e.g., office, home, industrial). In this tutorial, Mr. Marcus surveys the issues of cross-cultural communications, introduces culture dimensions, and discusses issues, with examples that are challenging analysts and designers worldwide. Developers are seeking to embrace this additional set of concerns that impact usability, usefulness, and appeal. Mr. Marcus will help show the way to improved user experience. Participants will learn about culture dimensions (e.g., Hofstede's power distance, individualism/collectivism, masculinity/femininity, uncertainty avoidance, and long-term time orientation), how they affect user-interface components (metaphors, mental models, navigation, interaction, and appearance), how to use culture models, the effect of culture on global Websites, a new best-of-breed culture dimensions set, and the ethnographic perspective. The main learning objectives of this tutorial will be: What are the dimensions of culture? How do culture dimensions affect user-interface components in collaborative environments? and What techniques can be used to analyze the effects of culture? TARGET AUDIENCE This tutorial is appropriate for novice researchers, academics, practitioners, and students, but should also be of interest to advanced practitioners who are unfamiliar with the specific topics to be covered in the presentation. TUTORIAL DURATION The tutorial material will be presented in a 2-hour session. INSTRUCTOR BIOGRAPHY Aaron Marcus received a BA in physics from Princeton (1965) and a BFA and MFA in graphic design from Yale Art School (1968). He is an internationally recognized authority on designing user interfaces, multimedia, documents, and online services. He co-authored Human Factors and Typography for More Readable Programs (1990) and The Cross-GUI Handbook for Multiplatform User Interface Design (1994), and he authored Graphic Design for Electronic Documents and User Interfaces (1992), all published by Addison-Wesley. Mr. Marcus was the world's first professional graphic designer to work in computer graphics (1967), to program a desktop publishing system (for the AT&T Picturephone, 1969-71), to design virtual realities (1971-73), to establish a computer-based graphic design firm (1982), and to receive the NCGA Industry Achievement Award for his contributions to computer graphics (1992). As President of Aaron Marcus and Associates, Inc., Emeryville, CA, and New York City, NY, he and his staff work with Fortune 500 companies and start-ups as planners, consultants, designers, and programmers. Mr. Marcus programmed and designed his first user interface in 1969 and wrote his first user interface design guidelines document in 1981. In the last 24 years, he and his staff have designed or helped to design at least 300 user interfaces. Mr. Marcus has given user interface design tutorials at CHI since 1990 and SIGGRAPH since 1980. At CHI-98, he organized the first babyface panel. AM+A has consulted on the Nokia Communicator 9000 and worked with Cisco/InfoGear on its Web-phone products. In 2000, Mr. Marcus published a case study of the user interface AM+A designed for Motorola's vehicle-navigation system, in Bergman (ed.), Information Appliances and Beyond, Morgan Kaufmann, San Francisco, 2000. His firm designed concepts for advanced mobile phones with PDA characteristics for Samsung, Korea. Mr. Marcus serves on the Boards of many publications and organizations, and is Editor-in-Chief of User Experience. Back to top