Using Local Optimality Criteria for Efficient Information Retrieval with Redundant Information Filters

Abstract

We consider information retrieval when the data - for instance, multimedia - is computationally expensive to fetch. Our approach uses "information filters" to considerably narrow the universe of possibilities before retrieval. We are especially interested in redundant information filters that save time over more general but more costly filters. Efficient retrieval requires that decisions must be made about the necessity, order, and concurrent processing of proposed filters (an "execution plan"). We develop simple polynomial-time local criteria for optimal execution plans and show that most forms of concurrency are suboptimal with information filters. Although the general problem of finding an optimal execution plan is likely to be exponential in the number of filters, we show experimentally that our local optimality criteria, used in a polynomial-time algorithm, nearly always find the global optimum with 15 filters or less, a sufficient number of filters for most applications. Our methods require no special hardware and avoid the high processor idleness that is characteristic of massive-parallelism solutions to this problem. We apply our ideas to an important application, information retrieval of captioned data using natural-language understanding, a problem for which the natural-language processing can be the bottleneck if not implemented well.