Data management systems, cloud services, probabilistic databases, and data pricing in Computer Science & Engineering at the University of Washington in Seattle.
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, image/video data management, data management for machine learning, ubiquitous computing, data integration, and data mining.
‘Demonstration of MaskSearch: Efficiently Querying Image Masks for Machine Learning Workflows’ by Lindsey Linxi Wei, Chung Yik Edward Yeung, Hongjian Yu, Jingchuan Zhou, Dong He, Magdalena Balazinska has been accepted at VLDB 2024.
Remy Wang received a runner up award for the 2024 Jim Gray SIGMOD Dissertation Award. Congratulations Remy!
Remy Wang published a research highlight paper called ‘From Binary Join to Free Join’ in SIGMOD Record. There is a nice technical perspective by Thomas Neumann.
Nicole Sullivan received a fellowship from the National Science Foundation Graduate Research Fellowship Program (NSF GRFP).
‘Optimizing Dataflow Systems for Scalable Interactive Visualization’ by Junran Yang, Hyekang Kevin Joo, Sai Yerramreddy, Dominik Moritz, and Leilani Battle to appear at SIGMOD 2024.
‘VOCALExplore: Pay-as-You-Go Video Data Exploration and Model Building’ by Maureen Daum, Enhao Zhang, Dong He, Stephen Mussmann, Brandon Haynes, Ranjay Krishna, and Magdalena Balazinska to appear at VLDB 2024.
‘EQUI-VOCAL: Synthesizing Queries for Compositional Video Events from Limited User Interactions’ by Enhao Zhang, Maureen Daum, Dong He, Brandon Haynes, Ranjay Krishna, and Magdalena Balazinska has been accepted at VLDB 2023.
‘Free Join: Unifying Worst-Case Optimal and Traditional Joins’ by Remy Wang, Max Willsey, and Dan Suciu to appear at SIGMOD 2023.
