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

TL;DR

This paper introduces polynomial-time local optimality criteria for designing efficient execution plans of redundant information filters in multimedia retrieval, significantly reducing computational costs without requiring special hardware.

Contribution

It develops simple local criteria for optimal filter execution plans, demonstrating that most concurrency strategies are suboptimal and providing an effective polynomial-time algorithm for near-optimal solutions.

Findings

Local optimality criteria nearly always find the global optimum for up to 15 filters

Most forms of concurrency are suboptimal with information filters

The method reduces retrieval time without special hardware

Abstract

We consider information retrieval when the data, for instance multimedia, is coputationally expensive to fetch. Our approach uses "information filters" to considerably narrow the universe of possiblities before retrieval. We are especially interested in redundant information filters that save time over more general but more costly filters. Efficient retrieval requires that decision 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 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 do not require 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 cpationed data using natural-language understanding, a problem for which the natural-language processing can be the bottleneck if not implemented well.