Efficient Learning Algorithms for Search via Cloud Computing

该提案旨在开发一种新的计算模式，以构建更有效的云计算方案，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要，旨在打破快速搜索要求和处理高维多媒体特征的负担之间的差距。众所周知，加载和计算高维数据都是昂贵的过程。所提出的范例采用小型树干的数据摘要，并使用这些摘要来估计搜索措施的下限和上限。 基于这些边界，该范例可以在加载之前过滤掉大量的数据样本，从而可以减少传输和计算开销。新的范式概括了谷歌？的MapReduce计算范式来处理高维数据的搜索任务，比通用计算范式更适合处理多媒体数据的应用。这个建议的智力价值是利用云计算提供的计算资源，并开发新的算法，以分布式和高效的方式执行多媒体数据搜索。 私家侦探？如果成功，云计算将彻底改变云计算的未来，并对整个社会产生巨大的影响。 问题的挑战及其潜在的回报和影响，如果成功的话，使这个建议非常适合EAGER计划。

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.