  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.
