Efficient Learning Algorithms for Search via Cloud Computing

通过云计算进行搜索的高效学习算法

• 批准号: 1049332
• 负责人: Thomas Huang
• 金额: $ 10万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-01 至 2011-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1049332&HistoricalAwards=false
• 关键词: Efficient; Learning; Algorithms; Search; via

This proposal aims to develop a new computing paradigm to build more effective cloud computing schemes for web-scale multimedia search and learning. It considers the need for algorithmic and systematic design, and aims to break down the gap between fast searching requirement and the burden of processing high dimensional multimedia features. It is well-known that loading and computing high dimensional data are both expensive procedures. The proposed paradigm employs data summary for small trunks and uses those summaries to estimate the lower bound and upper bound for searching measures. Based on these bounds, this paradigm can filter out a lot of data samples before loading them, and thus can reduce the transmission and computation overhead. The new paradigm generalizes Google?s MapReduce computing paradigm for the task of searching high dimensional data, and fits better the applications of processing multimedia data than the general-purpose computing paradigm. The intellectual merit of this proposal is to exploit the computing resources offered by cloud computing and to develop novel algorithms to perform the multimedia data search in a distributed and efficient manner. The PI?s ambition of making cloud computing suitable for high-dimensional numerical data, if successful, will revolutionize the future of cloud computing, and have a tremendous impact on society at large. The challenges of the problems and its potential payoff and impact, if successful, make this proposal ideally suited for the EAGER program.

该建议旨在开发一种新的计算范式，以建立更有效的云计算方案，以用于网络规模的多媒体搜索和学习。 它考虑了算法和系统设计的需求，并旨在打破快速搜索要求和处理高维多媒体功能的负担之间的差距。众所周知，加载和计算高维数据都是昂贵的过程。拟议的范式采用了小型树干的数据摘要，并使用这些摘要来估计下限和上限进行搜索措施。 基于这些范围，该范式可以在加载之前过滤大量数据样本，从而可以减少传输和计算开销。新的范式将Google的MapReduce计算范式推广到搜索高维数据的任务，并且比通用计算范式更好地适合处理多媒体数据的应用。该提案的智力优点是利用云计算提供的计算资源，并开发新型算法以分布式和有效的方式执行多媒体数据搜索。 PI的雄心勃勃的云计算适合于高维数字数据（如果成功）将彻底改变云计算的未来，并对整个社会产生巨大影响。 问题的挑战及其潜在的回报和影响（如果成功）使该提案非常适合渴望的计划。