Engineering Rank/Select Data Structures for Big-Alphabet Strings

Big-alphabet strings are common in several scenarios such as information retrieval and natural-language processing. The efficient storage and processing of such strings usually introduces several challenges that are not witnessed in smaller-alphabets strings. This paper studies the efficient implementation of one of the most effective approaches for dealing with big-alphabet strings, namely the \emph{alphabet-partitioning} approach. The main contribution is a compressed data structure that supports the fundamental operations rank and select efficiently. We show experimental results that indicate that our implementation outperforms the current realizations of the alphabet-partitioning approach. In particular, the time for operation select can be improved by about 80%, using only 11% more space than current alphabet-partitioning schemes. We also show the impact of our data structure on several applications, like the intersection of inverted lists (where improvements of up to 60% are achieved, using only 2% of extra space), the representation of run-length compressed strings, and the distributed-computation processing of rank and select operations.