The University of Washington's database group aims at broadening the focus of database and data management techniques beyond their traditional scope. We do both theoretical and systems work in areas such as probabilistic databases, stream processing, sensor-based monitoring, databases and the web, XML, data management for ubiquitous computing, data integration, and data mining.
Watch Dylan Hutchison’s recorded talk from the SIGMOD BeyondMR workshop on LaraDB: A Minimalist Kernel for Linear and Relational Algbera
Several SIGMOD 2017 Demonstation Awards! Shumo Chu, Daniel Li, and Chenglong Wang won the Best Demonstration Award for the demo Demonstration of the Cosette Automated SQL Prover (see Cosette); Maaz Ahmad earned a Demonstration Award Honorable Mention for the demo Optimizing Data-Intensive Applications Automatically By Leveraging Parallel Data Processing Frameworks; Brandon Haynes and Artem Minyaylov earned a Demonstration Award Honorable Mention for the demo VisualCloud Demonstration: A DBMS for Virtual Reality.
Parmita Mehta, Tomer Kaftan, and many co-authors had their paper Comparative Evaluation of Big-Data Systems on Scientific Image Analytics Workloads accepted into VLDB.