Efficient Learning Algorithms for Search via Cloud Computing

University Of Illinois At Urbana-Champaign, Urbana IL

Investigators

Abstract

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.