  Supporting top-k document retrieval queries on general text databases, that
is, finding the k documents where a given pattern occurs most frequently, has
become a topic of interest with practical applications. While the problem has
been solved in optimal time and linear space, the actual space usage is a
serious concern. In this paper we study various reduced-space structures that
support top-k retrieval and propose new alternatives. Our experimental results
show that our novel algorithms and data structures dominate almost all the
space/time tradeoff.
