The Web is no longer a static repository of documents; it is a dynamic repository of information that connects people with their passions, and on a more prosaic note, the applications they use in their personal and professional lives. How is the Web evolving as an information source, and how does this affect the future of information discovery? What are the implications of the rapid growth of social networks? How does the emergence of the Web as a delivery channel for services affect the future of software? Technically, these trends have given rise to a new wave of challenges, and led to vigorous research on a number of fronts ranging from social network analysis, information extraction and community information management, massively distributed storage and computing platforms, and placed a premium on hosted service architectures. In this talk, I will discuss these issues and outline some of the solutions that are beginning to emerge.

Raghu Ramakrishnan is Chief Scientist for Audience and Research Fellow at Yahoo!, and heads the Community Systems Group in Yahoo! Research. He is on leave from the University of Wisconsin-Madison, where he is Professor of Computer Sciences, and was founder and CTO of QUIQ, a company that pioneered question-answering communities, powering Ask Jeeves' AnswerPoint as well as customer-support for companies such as Compaq. His research has influenced query optimization in commercial database systems, and the design of window functions in SQL:1999. His paper on the Birch clustering algorithm received the SIGMOD 10-Year Test-of-Time award, and he has written the widely-used text "Database Management Systems" (with Johannes Gehrke). He is Chair of ACM SIGMOD, on the Board of Directors of ACM SIGKDD and the Board of Trustees of the VLDB Endowment, and has served as editor-in-chief of the Journal of Data Mining and Knowledge Discovery, associate editor of ACM Transactions on Database Systems, and the Database area editor of the Journal of Logic Programming. Dr. Ramakrishnan is a Fellow of the Association for Computing Machinery (ACM), and has received several awards, including a Distinguished Alumnus Award from IIT Madras, a Packard Foundation Fellowship, an NSF Presidential Young Investigator Award, and an ACM SIGMOD Contributions Award.

The database research community's appetite for new applications has led to increased interest in the data management needs of scientists. This area encompasses a huge range of applications, extending from public repositories of observational data such as the popular Sloan Digital Sky Survey to one-of-a-kind runs of simulation codes crafted by individual scientists. In this talk, we will survey the most common data management needs found in the hard sciences, describe the new database research challenges that arise from these needs, and outline ways to address some of these challenges.

Marianne Winslett has been a professor at the University of Illinois at Urbana-Champaign since 1987. Her current research interests include security in open systems and data management for scientific applications. She has served on the editorial boards of ACM Transactions on Database Systems and IEEE Transactions on Knowledge and Data Engineering, and is currently on the board of ACM Transactions on the Web. She is an ACM Fellow, a past vice-chair of ACM SIGMOD and the recipient of an NSF Presidential Young Investigator Award.

Data quality is a serious concern in complex industrial-scale databases, which often have thousands of tables and tens of thousands of columns. Commonly encountered problems include duplicates and default values in columns treated as keys, data inconsistencies, and poor quality join paths. Compounding the data quality problems are incomplete and out-of-date metadata about the database and the processes used to populate the database. These problems make the task of analyzing data particularly challenging. The Bellman data quality browser has been built to effectively address such problems. Bellman profiles the database and computes concise statistical summaries of the contents of the database to identify approximate keys, frequent values of a field (often default values), joinable fields, and to understand database dynamics (changes in a database over time). In this talk, I'll describe the technology underlying Bellman and how it is used to help make sense of complex databases.

Divesh Srivastava is the head of Database Research at AT&T Labs Research. He received his Ph.D. from the University of Wisconsin, Madison, and his Bachelor of Technology from the Indian Institute of Technology, Bombay, India. His current research interests include data quality and data stream management systems.