|Creating User Interfaces that Entice People to Manage Better Information
Much research in information management begins by asking how to manage a given information corpus. But information management systems can only be as good as the information they manage. They struggle and often fail to correctly infer meaning from large blobs of text and the mysterious actions and demands of users. And they are useless for managing information that is never captured.
Instead of accepting the existing information as an immutable condition, I will argue that there are significant opportunities to help and motivate people to improve the quality and quantity of information their tools manage, and to exploit that better information to benefit its users.
The greatest challenge in doing so is developing systems, and particularly user interfaces, that overcome humans' perverse reluctance to invest small present-moment effort for large future payoffs. Effective systems must minimize the effort needed to record high-quality information and maximize the perceived future benefits of that information investment.
I will support these ideas with examples covering structured data management and presentation, notetaking, collaborative filtering, and social media.
David R. Karger is a Professor of Electrical Engineering and Computer Science at MIT's Computer Science and Artificial Intelligence Laboratory. David earned his Ph.D. at Stanford University in 1994 and has since contributed to many areas of computer science, publishing in algorithms, machine learning, information retrieval, personal information management, networking, peer to peer systems, coding theory, the semantic web, and human-computer interaction.
An ongoing interest has been to make it easier for people to create, find, organize, manipulate, and share information. He formed and leads the Haystack group to investigate the topic. A major theme has been to free people from the straightjacket of existing applications by giving them the ability to define and manage their own information schema and visualizations on the desktop and on the web. He co-led MIT's SIMILE project, a collaboration with MIT Libraries and the World Wide Web consortium developing Semantic-Web tools to improve the management and retrieval of information at the institutional level.
|Data, Health, and Algorithmics: Computational Challenges for Biomedicine|
In the decade following the completion of the Human Genome Project in 2000, the cost of sequencing DNA fell by a factor of around a million, and continues to fall. Such sequencing is now a standard, ubiquitous tool for biomedical research, leading to widespread production of massive quantities of genetic data. Applications in clinical health include precise diagnosis of infection and disease, lifestyle management, and development of highly specific treatments - disruptive innovations that may drastically change medicine. Making use of this data involves a range of different kinds of analysis, such as identification of organisms, identification of which genes are active in a particular tissue, and assembly and annotation of the complete genomic sequence of an individual. Other biomedical technologies, from implants to imaging, are producing similarly vast quantities of data, opening further opportunities.
However, the volume and complexity of the data produced by these technologies presents a severe computational challenge. For example, a hundred gigabytes or more of sequencing data is required to construct the genome of a human individual, to monitor genomic changes in a cancer, to quantify activity within a cell, or to map the bacteria in a sample; and, currently, much of the analysis must be undertaken on large-memory clusters or supercomputers, an approach that greatly limits possible clinical uses. Breakthroughs in methods for search, storage, and analysis are required to keep pace with the flow of data, and to make use of the changes in biomedical knowledge that these technologies are creating. This keynote is an overview of some of these technologies and the new computational obstacles they have engendered, and reviews examples of algorithmic innovations and approaches currently being explored. These illustrate both the kinds of solutions that are required and the challenges that must be addressed to allow this abundant data to be fully exploited.
Justin Zobel is Professor of Computational Bioinformatics in the University of Melbourne's Department of Computer Science & Software Engineering, and leads the Computing for Life Sciences activities within National ICT Australia's Victorian Research Laboratory. Professor Zobel received his PhD from the University of Melbourne and for many years was based at RMIT University, where he led the Search Engine group. In the research community, Professor Zobel is best known for his role in the development of algorithms for efficient text retrieval, which underpin applications such as search engines deployed on the web. His research areas includes search, bioinformatics, fundamental algorithms and data structures, compression, and research methods. He is an author of two texts on postgraduate study and research methods. He is an Editor-in-Chief of the International Journal of Information Retrieval, and an associate editor of ACM Transactions on Information Systems, Information Processing & Management, and IEEE Transactions on Knowledge and Data Engineering.
|Ontology-based data management
Ontology-based data management aims at accessing and using data by means of a conceptual representation of the domain of interest in the underlying information system. Although this new paradigm provides several interesting features, and many of them have been already proved effective in managing complex information systems, several important issues remain open, and constitute stimulating challenges for the research community. In this talk we first provide an introduction to Ontology-based data management, illustrating the main ideas and techniques for using an ontology to access the data layer of an information system, and then we discuss several important issues that are still the subject of extensive investigations, including the need of inconsistency tolerant query answering methods, and the need of supporting update operations expressed over the ontology.
Maurizio Lenzerini is a Professor in Computer Science at Università di Roma La Sapienza. He is conducting research in data management, knowledge representation and reasoning, information integration, and service-oriented computing. He is the author of more than 250 publications in international conferences and journals, and has been invited speaker in many international conferences. He is currently the Chair of the Executive Committee of the ACM Symposium of Principles of Database Systems. He is a Fellow of the European Coordinating Committee for Artificial Intelligence (ECCAI), a Fellow of the Association for Computing Machinery (ACM), and the recipient of several research awards, including an IBM University Shared Research Award, and an IBM Faculty Award.