arXiv subjects

Alejandro Pacheco

Publications and source records attributed to Alejandro Pacheco.

3 recordsLinked to original sources

arXiv2024

Counting on General Run-Length Grammars

We introduce a data structure for counting pattern occurrences in texts compressed with any run-length context-free grammar. Our structure uses space proportional to the grammar size and counts the occurrences of a pattern of length $m$ in a text of length $n$ in time $O(m\log^{2+\epsilon} n)$, for any constant $\epsilon > 0$ chosen at indexing time. This is the first solution to an open problem posed by Christiansen et al.~[ACM TALG 2020] and enhances our abilities for computation over compressed data; we give an example application.

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arXiv2020

PHONI: Streamed Matching Statistics with Multi-Genome References

Computing the matching statistics of patterns with respect to a text is a fundamental task in bioinformatics, but a formidable one when the text is a highly compressed genomic database. Bannai et al. gave an efficient solution for this case, which Rossi et al. recently implemented, but it uses two passes over the patterns and buffers a pointer for each character during the first pass. In this paper, we simplify their solution and make it streaming, at the cost of slowing it down slightly. This means that, first, we can compute the matching statistics of several long patterns (such as whole human chromosomes) in parallel while still using a reasonable amount of RAM; second, we can compute matching statistics online with low latency and thus quickly recognize when a pattern becomes incompressible relative to the database.

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arXiv2020

Grammar-Compressed Indexes with Logarithmic Search Time

Let a text $T[1..n]$ be the only string generated by a context-free grammar with $g$ (terminal and nonterminal) symbols, and of size $G$ (measured as the sum of the lengths of the right-hand sides of the rules). Such a grammar, called a grammar-compressed representation of $T$, can be encoded using essentially $G\lg g$ bits. We introduce the first grammar-compressed index that uses $O(G\lg n)$ bits and can find the $occ$ occurrences of patterns $P[1..m]$ in time $O((m^2+occ)\lg G)$. We implement the index and demonstrate its practicality in comparison with the state of the art, on highly repetitive text collections.

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