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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Declarative Network Monitoring with an Underprovisioned Query Processor (Extended Version)
TL;DR: In this paper, the authors focus on passive network monitoring, an application in which the data rates typically exhibit a large peak-to-average ratio and propose to solve this problem by provisioning the query processor for typical data rates instead of much higher peak data rates.
Journal ArticleDOI
Data tweening: incremental visualization of data transforms
TL;DR: Through user studies, it is shown that data tweening allows users to efficiently comprehend data transforms, and also enables them to gain a better understanding of the underlying query operations.
Posted Content
A Fault-Tolerance Shim for Serverless Computing
Vikram Sreekanti,Chenggang Wu,Saurav Chhatrapati,Joseph E. Gonzalez,Joseph M. Hellerstein,Jose M. Faleiro +5 more
TL;DR: An atomic fault tolerance shim for serverless applications is presented in this paper, which interposes between a commodity FaaS platform and storage engine and ensures atomic visibility of updates by enforcing the read atomic isolation guarantee.
The design and implementation of declarative networks
TL;DR: It is demonstrated that NDlog can be used to express a large variety of network protocols in a handful of lines of program code, resulting in orders of magnitude reduction in code size.
Proceedings ArticleDOI
Out-of-core build of a topological data structure from polygon soup
TL;DR: This paper describes a new out-of-core algorithm that can build a topological data structure efficiently from very large data sets, improving performance by two orders of magnitude over a naive approach.