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.
We consider the plain and the compressed wavelet tree representations, and the straightforward and novel representations of 's succinct structure. We compare these alternatives with the original 's method (on plain and compressed wavelet trees), to test the hypothesis that adding 's structure is worth the extra space. Similarly, we include in the comparison the basic 's method (with their structure compressed or not) over 's @cite_18 sequence representation, to test the hypothesis that using 's method over the wavelet tree is worth compared to the brute force method over the fastest sequence representation @cite_18 . This brute force method is also at the core of the new proposal by @cite_22 .
@cite_18 @cite_22
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We consider a generalization of the problem of supporting rank and select queries on binary strings. Given a string of length n from an alphabet of size σ, we give the first representation that supports rank and access operations in O(lg lg σ) time, and select in O(1) time while using the optimal n lg σ + o(n lg σ) bits. The best known previous structure for this problem required O(lg σ) time, for general values of σ. Our results immediately improve the search times of a variety of text indexing methods. Let @math @math be a given set of @math string documents of total length @math , our task is to index @math , such that the @math most relevant documents for an online query pattern @math of length @math can be retrieved efficiently. We propose an index of size @math bits and @math query time for the basic relevance metric , where @math is the size (in bits) of a compressed full text index of @math , with @math time for searching a pattern of length @math . We further reduce the space to @math bits, however the query time will be @math , where @math is the alphabet size and @math is any constant.