Výsledky vyhledávání - "Shekita, Eugene"

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Column-Oriented Storage Techniques for MapReduce

Autor: Floratou, Avrilia, Patel, Jignesh, Shekita, Eugene, Tata, Sandeep

Users of MapReduce often run into performance problems when they scale up their workloads. Many of the problems they encounter can be overcome by applying techniques learned from over three decades of research on parallel DBMSs. However, translating

Externí odkaz: http://arxiv.org/abs/1105.4252

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Using Paxos to Build a Scalable, Consistent, and Highly Available Datastore

Autor: Rao, Jun, Shekita, Eugene J., Tata, Sandeep

Publikováno v: Proceedings of the VLDB Endowment (PVLDB), Vol. 4, No. 4, pp. 243-254 (2011)

Spinnaker is an experimental datastore that is designed to run on a large cluster of commodity servers in a single datacenter. It features key-based range partitioning, 3-way replication, and a transactional get-put API with the option to choose eith

Externí odkaz: http://arxiv.org/abs/1103.2408

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Impliance: A Next Generation Information Management Appliance

Autor: Bhattacharjee, Bishwaranjan, Ercegovac, Vuk, Glider, Joseph, Golding, Richard, Lohman, Guy, Markl, Volke, Pirahesh, Hamid, Rao, Jun, Rees, Robert, Reiss, Frederick, Shekita, Eugene, Swart, Garret

ably successful in building a large market and adapting to the changes of the last three decades, its impact on the broader market of information management is surprisingly limited. If we were to design an information management system from scratch,

Externí odkaz: http://arxiv.org/abs/cs/0612129

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High-performance implementation techniques for next-generation database systems

Autor: Shekita, Eugene J.

Thesis (Ph. D.)--University of Wisconsin--Madison, 1990.
Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 209-215).

Externí odkaz: http://catalog.hathitrust.org/api/volumes/oclc/23379166.html

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Impliance: A Next Generation Information Management Appliance

Autor: Glider, Joseph, Ercegovac, Vuk, Bhattacharjee, Bishwaranjan, Rao, Jun, Swart, Garret, Rees, Robert, Lohman, Guy, Golding, Richard, Reiss, Frederick, Shekita, Eugene, Pirahesh, Hamid, Markl, Volke

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1d374f0ae2ce0d148c302588db519af3
http://arxiv.org/abs/cs/0612129

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Scaling Distributed Machine Learning with the Parameter Server

Autor: Li, Mu, Andersen, David G., Park, Jun Woo, Smola, Alexander, Ahmed, Amr, Josifovski, Vanja, Long, James, Shekita, Eugene J., Bor-Yiing Su

We propose a parameter server framework for distributed machine learning problems. Both data and workloads are distributed over worker nodes, while the server nodes maintain globally shared parameters, represented as dense or sparse vectors and matri

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::16f700d5134efe9e4bb06751c68857b6

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