J
Joseph M. Hellerstein
Researcher at University of California, Berkeley
Publications - 313
Citations - 39413
Joseph M. Hellerstein is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Query optimization & Query language. The author has an hindex of 91, co-authored 300 publications receiving 37800 citations. Previous affiliations of Joseph M. Hellerstein include Carnegie Mellon University & IBM.
Papers
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Journal ArticleDOI
TAG: a Tiny AGgregation service for Ad-Hoc sensor networks
TL;DR: This work presents the Tiny AGgregation (TAG) service for aggregation in low-power, distributed, wireless environments, and discusses a variety of optimizations for improving the performance and fault tolerance of the basic solution.
Journal ArticleDOI
TinyDB: an acquisitional query processing system for sensor networks
TL;DR: This work evaluates issues in the context of TinyDB, a distributed query processor for smart sensor devices, and shows how acquisitional techniques can provide significant reductions in power consumption on the authors' sensor devices.
Journal ArticleDOI
Distributed GraphLab: a framework for machine learning and data mining in the cloud
Yucheng Low,Danny Bickson,Joseph E. Gonzalez,Carlos Guestrin,Aapo Kyrola,Joseph M. Hellerstein +5 more
TL;DR: GraphLab as discussed by the authors extends the GraphLab framework to the substantially more challenging distributed setting while preserving strong data consistency guarantees to reduce network congestion and mitigate the effect of network latency in the shared-memory setting.
Proceedings Article
TelegraphCQ: Continuous Dataflow Processing for an Uncertain World.
Sirish Chandrasekaran,Owen Cooper,Amol Deshpande,Michael J. Franklin,Joseph M. Hellerstein,Wei Hong,Sailesh Krishnamurthy,Samuel Madden,Vijayshankar Raman,Frederick Reiss,Mehul A. Shah +10 more
TL;DR: The next generation Telegraph system, called TelegraphCQ, is focused on meeting the challenges that arise in handling large streams of continuous queries over high-volume, highly-variable data streams and leverages the PostgreSQL open source code base.
Book ChapterDOI
Model-driven data acquisition in sensor networks
TL;DR: This paper enrichs interactive sensor querying with statistical modeling techniques, and demonstrates that such models can help provide answers that are both more meaningful, and, by introducing approximations with probabilistic confidences, significantly more efficient to compute in both time and energy.