ITR-RESCUE :: Responding to Crises and Unexpected Events

ITR-RESCUE is part of the California Institute for Telecommunications and Information Technology (Calit2) and its IT infrastructure is provided by Responsphere

RESCUE Seminar Series - April 17

MAFIA - A Mashup Fabric for Intranet Applications

Mehmet Altinel
IBM Research

Faculty Sponsor: Chen Li

Abstract:
MAFIA is a lightweight enterprise data integration service where line of business users can create and catalog high value data feeds for consumption by situational applications. MAFIA is inspired by the Web 2.0 mashup phenomenon. It consists of (1) a browser-based user-interface that allows for the specification of data mashups as data flow graphs using a set of MAFIA operators, (2) a server with an execution engine, as well as (3) APIs for searching, debugging, executing and managing mashups. MAFIA offers a framework and functionality for dynamic entity resolution, streaming and other higher value features particularly important in the enterprise domain. MAFIA is currently in perpetual beta in the IBM Intranet.

In this talk, I will present the MAFIA platform, and highlight main research issues that are being investigated. If time remains, I will give a quick demo of the prototype, and talk about relevant projects that I am involved in IBM Almaden Research Center.

Bio:
Mehmet Altinel is a member of Information Management department at IBM Almaden Research Center. Since he joined the department in 2001, he worked on numerous projects on database caching, database-integrated messaging systems, unified repository models for structured and unstructured data, and data management on the web. His research interests include databases, large-scale distributed database systems, data streams, dissemination, and data management problems on the web.

View detailed flyer, or for additional information on this series, e-mail us.

Rescue Distinguished Lecture Series - April 6

Self-Managing DBMS Technology: The AutoAdmin Experience

Surajit Chaudhuri
Senior Researcher, Microsoft Research

Faculty Sponsor: Sharad Mehrotra

Abstract:
The cost of ownership of any commercial database system is significant. The AutoAdmin project at Microsoft Research was initiated (well before the term Autonomic Computing became a buzzword) to develop techniques to reduce the overhead of database administration. Our goal was to make it easier to monitor the server and develop self-tuning techniques for performance management. The technology form this project has been incorporated in the Microsoft SQL Server 2005 (and earlier releases - SQL Server 7.0 and SQL Server 2000). This talk will take a look at some fo the past reserach results and discuss challenges and opportunities in self-tuning DBMS research.

Bio:
Surajit Chaudhuri is a Senior Researcher and leads the Data Management and Exploration Group at Microsoft Research. His areas of interest include self-tuning database systems, query optimization, data cleaning and other tools for data integration, understanding synergy between IR and DBMS. As his work outside of database research, he led the development of CMT, a conference management service hosted by Microsoft Research since 1999 for the academic community. Surajit has a Ph.D. from Stanford University and is an ACM Fellow. He was awarded the SIGMOD Contributions Award in 2004.

View detailed flyer, or for additional information on this series, please e-mail us. (top)

RESCUE Distinguished Lecture Series - March 26

Scaling Computer Games to Epic Proportions

Johannes Gehrke
Professor, Department of Computer Science
Associate Director, Cornell Theory Center
Cornell University

Faculty Sponsor: Sharad Mehrotra

Abstract:
This talk will introduce scalability for computer games as the next frontier for techniques from data management. A very important aspect of computer games is the artificial intelligence (AI) of non-player characters. To create interesting AI in games today, developers or players can create complex, dynamic behavior for a very small number of characters, but neither the game engines nor the style of AI programming enables intelligent behavior that scales to a very large number of non-player characters.

It will introduce a first step towards truly scalable AI in computer games by modeling game AI as a data management problem, and will present a highly expressive scripting language SGL (for Scalable Gaming Language) that provides game designers and players with a data-driven AI scheme for customizing behavior for individual non-player characters. The use sophisticated query processing and indexing techniques allows us to efficiently execute large numbers of SGL scripts, thus providing a framework for games with a truly epic number of non-player characters. The talk will conclude with an outlook how our techniques can be used to also achieve significant scalability in large-scale simulations.

This talk describes joint work with Alan Demers (Cornell), Christoph Koch (Saarland University), Rajmohan Rajagopalan (Cornell), and Walker White (Cornell).

BIO:
Johannes Gehrke is an Associate Professor in the Department of Computer Science at Cornell University and an Associate Director of the Cornell Theory Center. Johannes' research interests are in the areas of data mining, search, data privacy, complex event processing, and applications of database and data mining technology to marketing and the sciences. Johannes has received a National Science Foundation Career Award, an Arthur P. Sloan Fellowship, an IBM Faculty Award, the Cornell College of Engineering James and Mary Tien Excellence in Teaching Award, and the Cornell University Provost's Award for Distinguished Scholarship. He is the author of numerous publications on data mining and database systems, and he co-authored the undergraduate textbook Database Management Systems (McGrawHill (2002), currently in its third edition), used at universities all over the world.

Johannes was co-Chair of the 2003 ACM SIGKDD Cup, Program co-Chair of the 2004 ACM International Conference on Knowledge Discovery and Data Mining (KDD 2004), and he is Program Chair of the 33rd International Conference on Very Large Data Bases (VLDB 2007).