Beth Trushkowsky
University of California, Berkeley
15 Papers
143 Citations
Beth Trushkowsky is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Query language & Crowdsourcing. The author has an hindex of 9, co-authored 14 publications. Previous affiliations of Beth Trushkowsky include Duke University & Harvey Mudd College.
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Papers
•Posted Content
SCADS: Scale-Independent Storage for Social Computing Applications
Michael Armbrust,Armando Fox,David A. Patterson,Nick Lanham,Beth Trushkowsky,Jesse Trutna,Haruki Oh +6 more
TL;DR: SCADS as discussed by the authors is a distributed key-value store that allows the developer to declaratively state application specific consistency requirements, takes advantage of utility computing to provide cost effective scale-up and scale-down, and will use machine learning models to introspectively anticipate performance problems and predict the resource requirements of new queries before execution.
•Proceedings Article
SCADS: Scale-Independent Storage for Social Computing Applications.
Michael Armbrust,Armando Fox,David A. Patterson,Nick Lanham,Beth Trushkowsky,Jesse Trutna,Haruki Oh +6 more
- 01 Jan 2009
TL;DR: A new architecture, SCADS, is proposed that allows the developer to declaratively state application specific consistency requirements, takes advantage of utility computing to provide cost effective scale-up and scale-down, and will use machine learning models to introspectively anticipate performance problems and predict the resource requirements of new queries before execution.
56
Answering enumeration queries with the crowd
TL;DR: This work develops statistical tools that enable users and systems developers to reason about query completeness and can also help drive query execution and crowdsourcing strategies.
15
•Proceedings Article
Dynamic Filter: Adaptive Query Processing with the Crowd
Doren Lan,Katherine Reed,Austin Shin,Beth Trushkowsky +3 more
- 21 Sep 2017
TL;DR: Dynamic Filter is presented, an adaptive query processing algorithm that dynamically changes the order in which criteria are evaluated based on observations while the query is running, and can effectively adapt the processing order and approach the performance of a “clairvoyant” algorithm.
12
PIQL: a performance insightful query language
Michael Armbrust,Stephen Tu,Armando Fox,Michael J. Franklin,David A. Patterson,Nick Lanham,Beth Trushkowsky,Jesse Trutna +7 more
- 06 Jun 2010
TL;DR: PIQL is demonstrated, a Performance Insightful Query Language that allows developers to express many of the queries found on these websites, while still providing strict bounds on the number of I/O operations for any query.