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Adam Silberstein
Researcher at Yahoo!
Publications - 51
Citations - 6127
Adam Silberstein is an academic researcher from Yahoo!. The author has contributed to research in topics: Wireless sensor network & Key distribution in wireless sensor networks. The author has an hindex of 23, co-authored 50 publications receiving 5517 citations. Previous affiliations of Adam Silberstein include Duke University & LinkedIn.
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Proceedings ArticleDOI
Benchmarking cloud serving systems with YCSB
TL;DR: This work presents the "Yahoo! Cloud Serving Benchmark" (YCSB) framework, with the goal of facilitating performance comparisons of the new generation of cloud data serving systems, and defines a core set of benchmarks and reports results for four widely used systems.
Journal ArticleDOI
PNUTS: Yahoo!'s hosted data serving platform
Brian F. Cooper,Raghu Ramakrishnan,Utkarsh Srivastava,Adam Silberstein,Philip Bohannon,Hans-Arno Jacobsen,Nick Puz,Daniel Weaver,Ramana Yerneni +8 more
TL;DR: PNUTS provides data storage organized as hashed or ordered tables, low latency for large numbers of concurrent requests including updates and queries, and novel per-record consistency guarantees and utilizes automated load-balancing and failover to reduce operational complexity.
Proceedings ArticleDOI
Constraint chaining: on energy-efficient continuous monitoring in sensor networks
TL;DR: This work adds enhancements to CONCH to build in redundant constraints and provide a method to interpret the resulting reports in case of uncertainty, and experimentally evaluates CONCH's effectiveness against competing schemes in a number of interesting scenarios.
Proceedings ArticleDOI
A Sampling-Based Approach to Optimizing Top-k Queries in Sensor Networks
TL;DR: This work proposes to use samples of past sensor readings to formulate the problem of optimizing approximate top-k queries under an energy constraint as a linear program, and demonstrates the power and flexibility of this sampling-based approach by developing a series of topk query planning algorithms with linear programming.
Proceedings ArticleDOI
Sailfish: a framework for large scale data processing
TL;DR: The Sailfish design enables auto-tuning functionality that handles changes in data volume and skewed distributions effectively, thereby addressing an important practical drawback of Hadoop, which in contrast relies on programmers to configure system parameters appropriately for each job, for each input dataset.