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pro vyhledávání: '"Elmore, Aaron J."'
Autor:
Xia, Siyuan, Zhu, Zhiru, Zhu, Chris, Zhao, Jinjin, Chard, Kyle, Elmore, Aaron J., Foster, Ian, Franklin, Michael, Krishnan, Sanjay, Fernandez, Raul Castro
Pooling and sharing data increases and distributes its value. But since data cannot be revoked once shared, scenarios that require controlled release of data for regulatory, privacy, and legal reasons default to not sharing. Because selectively contr
Externí odkaz:
http://arxiv.org/abs/2305.03842
We propose MindPalace, a prototype of a versioned database for efficient collaborative data management. MindPalace supports offline collaboration, where users work independently without real-time correspondence. The core of MindPalace is a critical s
Externí odkaz:
http://arxiv.org/abs/2110.01778
Data loading has been one of the most common performance bottlenecks for many big data applications, especially when they are running on inefficient human-readable formats, such as JSON or CSV. Parsing, validating, integrity checking and data structu
Externí odkaz:
http://arxiv.org/abs/2102.11793
Today, data analysts largely rely on intuition to determine whether missing or withheld rows of a dataset significantly affect their analyses. We propose a framework that can produce automatic contingency analysis, i.e., the range of values an aggreg
Externí odkaz:
http://arxiv.org/abs/2004.04139
As neural networks are increasingly employed in machine learning practice, how to efficiently share limited training resources among a diverse set of model training tasks becomes a crucial issue. To achieve better utilization of the shared resources,
Externí odkaz:
http://arxiv.org/abs/2002.02885
Carefully selected materialized views can greatly improve the performance of OLAP workloads. We study using deep reinforcement learning to learn adaptive view materialization and eviction policies. Our insight is that such selection policies can be e
Externí odkaz:
http://arxiv.org/abs/1903.01363
Advances in deep learning have greatly widened the scope of automatic computer vision algorithms and enable users to ask questions directly about the content in images and video. This paper explores the necessary steps towards a future Visual Data Ma
Externí odkaz:
http://arxiv.org/abs/1812.07607
Autor:
Bhardwaj, Anant, Bhattacherjee, Souvik, Chavan, Amit, Deshpande, Amol, Elmore, Aaron J., Madden, Samuel, Parameswaran, Aditya G.
Relational databases have limited support for data collaboration, where teams collaboratively curate and analyze large datasets. Inspired by software version control systems like git, we propose (a) a dataset version control system, giving users the
Externí odkaz:
http://arxiv.org/abs/1409.0798
Publikováno v:
Proceedings of the VLDB Endowment (PVLDB), Vol. 5, No. 11, pp. 1459-1470 (2012)
We present a framework for concurrency control and availability in multi-datacenter datastores. While we consider Google's Megastore as our motivating example, we define general abstractions for key components, making our solution extensible to any s
Externí odkaz:
http://arxiv.org/abs/1208.0270
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