Everyone’s invited! For the details, please see the dbmusings page.
Archive for the ‘DB Musing Lectures’ Category
Zachary Ives (University of Pennsylvania) will discuss “ORCHESTRA: Rapid, Collaborative Sharing of Dynamic Data” on Friday, October 6, at 11:00 am, in EBII 3211 — NCSU Centennial Campus.
The cost of ownership of any commercial database system is significant. The AutoAdmin project at Microsoft Research was started in late 1996 (well before the term Autonomic Computing became fashionable) 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 from this project has been incorporated in the Database Tuning Advisor tool in upcoming SQL Server 2005 release (and earlier as Index Tuning Wizard in SQL Server 7.0 and SQL Server 2000). This talk will take a look at some of the past research results and discuss challenges and opportunities in self-tuning DBMS research.
University of Toronto
The problem of data semantics is establishing and maintaining a correspondence between a data source (e.g., a database of an XML document) and its intended subject matter. We review the long history of the problem in Databases, and contrast it with current work on the Semantic Web. We then propose two new directions for research and sketch some open questions. This is joint work with Alex Borgida (Rutgers University), published in Borgida, A., Mylopoulos, J., Data Semantics Revisited, Proceedings VLDB Workshop on the Semantic Web and Databases (SWDB-04), Toronto, August 2004, Springer-Verlag LNCS.
University of Wisconsin – Madison
The EDAM project is a collaborative effort between computer scientists and environmental chemists at Carleton College and UW-Madison. The goal is to develop data mining techniques for advancing the state of the art in analyzing atmospheric aerosol datasets. The traditional approach for particle measurement, which is the collection of bulk samples of particulates on filters, is not adequate for studying particle dynamics and real-time correlations. This has led to the development of a new generation of real-time instruments that provide continuous or semi-continuous streams of data about certain aerosol properties. However, these instruments have added a significant level of complexity to atmospheric aerosol data, and dramatically increased the amounts of data to be collected, managed, and analyzed. We are investigating techniques for automatically labeling mass spectra from different kinds of aerosol mass spectrometers, and then analyzing and exploring the rich spatiotemporal information collected from multiple geographically distributed instruments. This talk presents an overview of some novel data mining problems, describe some of the techniques we are developing to address them, and discuss the broader applicability of these techniques to problems from other domains.