AF: EAGER: Data Streaming with a View towards Cloud Computing
AF:EAGER:面向云计算的数据流
基本信息
- 批准号:1650992
- 负责人:
- 金额:$ 30万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2016
- 资助国家:美国
- 起止时间:2016-09-01 至 2019-02-28
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
The ability to efficiently and effectively process big data is becoming central to modern life. Increasingly, much of this processing takes place "in the cloud", on large clusters of powerful servers, which provide computational power far exceeding those available on a weak client, e.g., an end-user's personal computer. Many big data problems require time-and-space-efficient processing of massive data streams generated almost continuously: examples include financial transactions, medical records, and scientific experimentaldata. Access to a cloud computing service makes tractable a number of data streaming problems that would otherwise be intractable for a weak client.Existing cloud computing services do not give clients a guarantee that their computations will be executed error-free. In the course of this project, the PI will work to design and study the theoretical foundations of techniques that would enable a weak client to trust results arrived at when working withsuch a service. New algorithms designed during this project could have an impact on the practice of cloud computing and data streaming systems. The project will enable the training of graduate and undergraduate students in theoretical computer science research and exposition, and in experimentally studying algorithmic ideas relevant to such trustworthy cloud computing.The project has two major threads. The first is about algorithm design, wherein the PI will seek new or improved algorithms (in terms of space usage and communication costs) in the above weak-client/powerful-server setting. In particular, the PI will revisit such fundamental problems as norm estimation and clustering. The second thread is complexity-theoretic: the PI will seek to understand the limitations of this computational setting, most likely through proving new lower bounds in communication complexity. This thread naturally leads to open questions about Arthur-Merlin communication, a topic long known to be difficult enough that even small advances could be significant breakthroughs.
高效处理大数据的能力正在成为现代生活的核心。越来越多的这种处理发生在“云中”,在强大服务器的大型集群上,其提供的计算能力远远超过弱客户端上可用的计算能力,例如,最终用户的个人电脑。许多大数据问题需要对几乎连续生成的海量数据流进行时空高效的处理:例如金融交易、医疗记录和科学实验数据。访问云计算服务使得许多数据流问题变得易于处理,而这些问题对于弱客户端来说是难以解决的。现有的云计算服务不能保证客户端的计算将无错误地执行。在这个项目的过程中,PI将致力于设计和研究技术的理论基础,使弱客户端能够信任在使用此类服务时获得的结果。该项目期间设计的新算法可能会对云计算和数据流系统的实践产生影响。该项目将使研究生和本科生能够在理论计算机科学研究和展示方面进行培训,并在实验中研究与这种值得信赖的云计算相关的算法思想。第一个是关于算法设计,其中PI将在上述弱客户端/强服务器设置中寻求新的或改进的算法(在空间使用和通信成本方面)。特别是,PI将重新审视这些基本问题,如范数估计和聚类。第二条线索是复杂性理论:PI将寻求理解这种计算设置的局限性,最有可能的是通过证明通信复杂性的新下限。这条线索自然会引出关于亚瑟-梅林交流的公开问题,这个话题长期以来一直很难,即使是微小的进步也可能是重大的突破。
项目成果
期刊论文数量(3)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Maximum Matching in Two, Three, and a Few More Passes Over Graph Streams
图形流上两次、三次以及更多次的最大匹配
- DOI:10.4230/lipics.approx-random.2017.15
- 发表时间:2017
- 期刊:
- 影响因子:0
- 作者:Kale, Sagar;Tirodkar, Sumedh
- 通讯作者:Tirodkar, Sumedh
Towards Tighter Space Bounds for Counting Triangles and Other Substructures in Graph Streams
用于计算图流中的三角形和其他子结构的更紧密的空间界限
- DOI:
- 发表时间:2017
- 期刊:
- 影响因子:0
- 作者:Bera, Suman K;Chakrabarti, Amit
- 通讯作者:Chakrabarti, Amit
Certifying Equality With Limited Interaction
通过有限的互动来证明平等
- DOI:10.1007/s00453-016-0163-6
- 发表时间:2016
- 期刊:
- 影响因子:1.1
- 作者:Brody, Joshua;Chakrabarti, Amit;Kondapally, Ranganath;Woodruff, David P.;Yaroslavtsev, Grigory
- 通讯作者:Yaroslavtsev, Grigory
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Amit Chakrabarti其他文献
Nearly Private Information Retrieval
近乎隐私的信息检索
- DOI:
10.1007/978-3-540-74456-6_35 - 发表时间:
2007 - 期刊:
- 影响因子:0
- 作者:
Amit Chakrabarti;Anna Shubina - 通讯作者:
Anna Shubina
Finding missing items requires strong forms of randomness
寻找丢失的物品需要很强的随机性
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Amit Chakrabarti;Manuel Stoeckl - 通讯作者:
Manuel Stoeckl
W57. PATHWAYS TO RESILIENCE AND MENTAL-HEALTH [PARAM]: A NEURODEVELOPMENTAL COHORT FROM INDIA
W57. 复原力和心理健康的途径[参数]:来自印度的一个神经发育队列
- DOI:
10.1016/j.euroneuro.2024.08.266 - 发表时间:
2024-10-01 - 期刊:
- 影响因子:6.700
- 作者:
Vivek Benegal;Amit Chakrabarti;Bharath Holla;Debasish Basu;Eesha Sharma;Meera Purushottam;Jayant Mahadevan;Nishant Goyal;Naresh Nebhinani;Aniruddha Basu;Rajkumar Lenin Singh;Sourav Khanra;Biju Viswanath - 通讯作者:
Biju Viswanath
Verifiable Stream Computation and Arthur-Merlin Communication
可验证的流计算和 Arthur-Merlin 通信
- DOI:
- 发表时间:
2015 - 期刊:
- 影响因子:0
- 作者:
Amit Chakrabarti;Graham Cormode;A. Mcgregor;J. Thaler;Suresh Venkatasubramanian - 通讯作者:
Suresh Venkatasubramanian
Growth trajectories for executive and social cognitive abilities in an Indian population sample: Impact of demographic and psychosocial determinants.
印度人口样本中执行和社会认知能力的增长轨迹:人口和社会心理决定因素的影响。
- DOI:
10.1016/j.ajp.2023.103475 - 发表时间:
2023 - 期刊:
- 影响因子:9.5
- 作者:
E. Sharma;G. Ravi;K. Kumar;K. Thennarasu;J. Heron;Matthew Hickman;N. Vaidya;B. Holla;Madhavi Rangaswamy;U. Mehta;M. Krishna;Amit Chakrabarti;D. Basu;S. Nanjayya;R. Singh;Roshan Lourembam;K. Kumaran;R. Kuriyan;S. Kurpad;K. Kartik;K. Kalyanram;S. Desrivières;G. Barker;D. P. Orfanos;M. Toledano;M. Purushottam;R. Bharath;P. Murthy;Sanjeev Jain;G. Schumann;V. Benegal - 通讯作者:
V. Benegal
Amit Chakrabarti的其他文献
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{{ truncateString('Amit Chakrabarti', 18)}}的其他基金
AF: CIF: Small: Communication complexity techniques beyond classical information theory
AF:CIF:小:超越经典信息论的通信复杂性技术
- 批准号:
2006589 - 财政年份:2020
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
AF: Small: Collaborative Research: New Challenges in Graph Stream Algorithms and Related Communication Games
AF:小:协作研究:图流算法和相关通信游戏的新挑战
- 批准号:
1907738 - 财政年份:2019
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
AF: Small: Foundational Research in Communication Complexity and Its Applications
AF:小型:通信复杂性及其应用的基础研究
- 批准号:
1217375 - 财政年份:2012
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
DC: Small: Data Streaming through a Complexity-Theoretic Lens
DC:小:通过复杂性理论镜头进行数据流
- 批准号:
0916565 - 财政年份:2009
- 资助金额:
$ 30万 - 项目类别:
Standard Grant
CAREER: Information Theoretic Methods in Communication and Computational Complexity
职业:通信和计算复杂性中的信息论方法
- 批准号:
0448277 - 财政年份:2005
- 资助金额:
$ 30万 - 项目类别:
Continuing Grant
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