20 research outputs found
Engineering Compact Data Structures for Rank and Select Queries on Bit Vectors
Bit vectors are fundamental building blocks of succinct data structures used in compressed text indices, e.g., in the form of the wavelet trees. Here, two types of queries are of interest: rank and select queries. In practice, the smallest (uncompressed) rank and select data structure cs-poppy has a space overhead of ≈ 3.51 % [Zhou et al. SEA 2013] [26]. Using the same overhead, we present a data structure that can answer queries up to 8 % (rank) and 16.5 % (select) faster compared with cs-poppy
On the Benefit of Merging Suffix Array Intervals for Parallel Pattern Matching
We present parallel algorithms for exact and approximate pattern matching
with suffix arrays, using a CREW-PRAM with processors. Given a static text
of length , we first show how to compute the suffix array interval of a
given pattern of length in
time for . For approximate pattern matching with differences or
mismatches, we show how to compute all occurrences of a given pattern in
time, where is the size of the alphabet
and . The workhorse of our algorithms is a data structure
for merging suffix array intervals quickly: Given the suffix array intervals
for two patterns and , we present a data structure for computing the
interval of in sequential time, or in
parallel time. All our data structures are of size bits (in addition to
the suffix array)
Faster Block Tree Construction
The block tree [Belazzougui et al. J. Comput. Syst. Sci. \u2721] is a compressed text index that can answer access (extract a character at a position), rank (number of occurrences of a specified character in a prefix of the text), and select (size of smallest prefix such that a specified character has a specified rank) queries. It requires O(zlog(n/z)) words of space, where z is the number of Lempel-Ziv factors of the text. For some highly repetitive inputs, a block tree can require as little as 0.015 bits per character of the text. Small values of z make the block tree a space-efficient alternative to the wavelet tree, which is another index for these three types of queries. While wavelet trees can be constructed fast in practice, up so far compressed versions of the wavelet tree only leverage statistical compression, meaning that they are blind to spaced repetitions.
To make block trees usable in practice, a first step is to find ways in constructing them efficiently. We address this problem by presenting a practically efficient construction algorithm for block trees, which is up to an order of magnitude faster than previous implementations. Additionally, we parallelize our implementation, making it the first block tree construction implementation that works in parallel in shared memory
Scalable Construction of Text Indexes with Thrill
The suffix array is the key to efficient solutions for myriads of string processing problems in different application domains, like data compression, data mining, or bioinformatics. With the rapid growth of available data, suffix array construction algorithms have to be adapted to advanced computational models such as external memory and distributed computing. In this article, we present five suffix array construction algorithms utilizing the new algorithmic big data batch processing framework Thrill, which allows scalable processing of input sizes on distributed systems in orders of magnitude that have not been considered before
On the Benefit of Merging Suffix Array Intervals for Parallel Pattern Matching
We present parallel algorithms for exact and approximate pattern matching with suffix arrays, using a CREW-PRAM with p processors. Given a static text of length n, we first show how to compute the suffix array interval of a given pattern of length m in O(m/p + lg p + lg lg p * lg lg n) time for p <= m. For approximate pattern matching with k differences or mismatches, we show how to compute all occurrences of a given pattern in O((m^k sigma^k)/p max (k, lg lg n) + (1+m/p) lg p * lg lg n + occ} time, where sigma is the size of the alphabet and p <= sigma^k m^k. The workhorse of our algorithms is a data structure for merging suffix array intervals quickly: Given the suffix array intervals for two patterns P and P\u27, we present a data structure for computing the interval of PP\u27 in O(lg lg n) sequential time, or in O(1 + lg_p lg n) parallel time. All our data structures are of size O(n) bits (in addition to the suffix array)