New space/time tradeoffs for top-k document retrieval on sequences

We address the problem of indexing a collectionD = {T1,T2, ...TD} of D string documents of total length n, so that we can efficiently answer top-k queries: retrieve k documents most relevant to a pattern P of length p given at query time. There exist linear-space data structures, that is, using O(n) words, that answer such queries in optimal O(p + k) time for an ample set of notions of relevance. However, using linear space is not sufficiently good for large text collections. In this paper we explore how far the space/time tradeoff for this problem can be pushed. We obtain three results: (1) When relevance is measured as term frequency (number of times P appears in a document Ti), an index occupying |CSA|+o(n) bits answers the query in time O(tsearch(p)+k lg2 k lg n), where CSA is a compressed suffix array indexing D, tsearch is its time to find the suffix array interval of P, and ε > 0 is any constant. (2) With the same measure of relevance, an index occupying |CSA| + n lg D + o(n lgσ + n lg D) bits answers the query in time O(tsearch(p) + k lg∗ k), where lg∗ k is the iterated logarithm of k. (3) When the relevance depends only on the documents, an index occupying |CSA|+ O(n lg lg n) bits answers the query in O(tsearch(p) + k tSA) time, where tSA is the time the CSA needs to retrieve a suffix array cell. On our way, we obtain some other results of independent interest.