University of Washington

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Data acquisition is outpacing data analysis in every scientific discipline. As a result, the process of *doing* science is fundamentally changing: Discoveries are increasingly made by querying large public databases rather than conducting primary experiments (consider GenBank and SDSS, for example). This shift mandates collaborations with computer scientists and technology experts, especially those in the database community. As part of that community, my group builds systems that facilitate this new kind of data-intensive science. Our recent work focuses on a database-as-a-service systems targeting scientists (SQLShare and Myria). In the past, I've worked on a query language for "gr.

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Aktuellste Beliebteste SlideShares

University of Washington

Database Agnostic Workload Management (CIDR 2019)

Data Responsibly: The next decade of data science

Thoughts on Big Data and more for the WA State Legislature

The Other HPC: High Productivity Computing in Polystore Environments

Big Data + Big Sim: Query Processing over Unstructured CFD Models

Data, Responsibly: The Next Decade of Data Science

Democratizing Data Science in the Cloud

Science Data, Responsibly

Data Science, Data Curation, and Human-Data Interaction

The Other HPC: High Productivity Computing

Urban Data Science at UW

Intro to Data Science Concepts

Big Data Talent in Academic and Industry R&D

Big Data Middleware: CIDR 2015 Gong Show Talk, David Maier, Bill Howe

Data Science and Urban Science @ UW

MMDS 2014: Myria (and Scalable Graph Clustering with RelaxMap)

XLDB South America Keynote: eScience Institute and Myria

Myria: Analytics-as-a-Service for (Data) Scientists

Big Data Curricula at the UW eScience Institute, JSM 2013

eResearch New Zealand Keynote