35,997 research outputs found
Tree Compression with Top Trees Revisited
We revisit tree compression with top trees (Bille et al, ICALP'13) and
present several improvements to the compressor and its analysis. By
significantly reducing the amount of information stored and guiding the
compression step using a RePair-inspired heuristic, we obtain a fast compressor
achieving good compression ratios, addressing an open problem posed by Bille et
al. We show how, with relatively small overhead, the compressed file can be
converted into an in-memory representation that supports basic navigation
operations in worst-case logarithmic time without decompression. We also show a
much improved worst-case bound on the size of the output of top-tree
compression (answering an open question posed in a talk on this algorithm by
Weimann in 2012).Comment: SEA 201
A Universal Parallel Two-Pass MDL Context Tree Compression Algorithm
Computing problems that handle large amounts of data necessitate the use of
lossless data compression for efficient storage and transmission. We present a
novel lossless universal data compression algorithm that uses parallel
computational units to increase the throughput. The length- input sequence
is partitioned into blocks. Processing each block independently of the
other blocks can accelerate the computation by a factor of , but degrades
the compression quality. Instead, our approach is to first estimate the minimum
description length (MDL) context tree source underlying the entire input, and
then encode each of the blocks in parallel based on the MDL source. With
this two-pass approach, the compression loss incurred by using more parallel
units is insignificant. Our algorithm is work-efficient, i.e., its
computational complexity is . Its redundancy is approximately
bits above Rissanen's lower bound on universal compression
performance, with respect to any context tree source whose maximal depth is at
most . We improve the compression by using different quantizers for
states of the context tree based on the number of symbols corresponding to
those states. Numerical results from a prototype implementation suggest that
our algorithm offers a better trade-off between compression and throughput than
competing universal data compression algorithms.Comment: Accepted to Journal of Selected Topics in Signal Processing special
issue on Signal Processing for Big Data (expected publication date June
2015). 10 pages double column, 6 figures, and 2 tables. arXiv admin note:
substantial text overlap with arXiv:1405.6322. Version: Mar 2015: Corrected a
typ
On optimally partitioning a text to improve its compression
In this paper we investigate the problem of partitioning an input string T in
such a way that compressing individually its parts via a base-compressor C gets
a compressed output that is shorter than applying C over the entire T at once.
This problem was introduced in the context of table compression, and then
further elaborated and extended to strings and trees. Unfortunately, the
literature offers poor solutions: namely, we know either a cubic-time algorithm
for computing the optimal partition based on dynamic programming, or few
heuristics that do not guarantee any bounds on the efficacy of their computed
partition, or algorithms that are efficient but work in some specific scenarios
(such as the Burrows-Wheeler Transform) and achieve compression performance
that might be worse than the optimal-partitioning by a
factor. Therefore, computing efficiently the optimal solution is still open. In
this paper we provide the first algorithm which is guaranteed to compute in
O(n \log_{1+\eps}n) time a partition of T whose compressed output is
guaranteed to be no more than -worse the optimal one, where
may be any positive constant
Compressed Text Indexes:From Theory to Practice!
A compressed full-text self-index represents a text in a compressed form and
still answers queries efficiently. This technology represents a breakthrough
over the text indexing techniques of the previous decade, whose indexes
required several times the size of the text. Although it is relatively new,
this technology has matured up to a point where theoretical research is giving
way to practical developments. Nonetheless this requires significant
programming skills, a deep engineering effort, and a strong algorithmic
background to dig into the research results. To date only isolated
implementations and focused comparisons of compressed indexes have been
reported, and they missed a common API, which prevented their re-use or
deployment within other applications.
The goal of this paper is to fill this gap. First, we present the existing
implementations of compressed indexes from a practitioner's point of view.
Second, we introduce the Pizza&Chili site, which offers tuned implementations
and a standardized API for the most successful compressed full-text
self-indexes, together with effective testbeds and scripts for their automatic
validation and test. Third, we show the results of our extensive experiments on
these codes with the aim of demonstrating the practical relevance of this novel
and exciting technology
Regular Expression Search on Compressed Text
We present an algorithm for searching regular expression matches in
compressed text. The algorithm reports the number of matching lines in the
uncompressed text in time linear in the size of its compressed version. We
define efficient data structures that yield nearly optimal complexity bounds
and provide a sequential implementation --zearch-- that requires up to 25% less
time than the state of the art.Comment: 10 pages, published in Data Compression Conference (DCC'19
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