3,139 research outputs found
Fast and Tiny Structural Self-Indexes for XML
XML document markup is highly repetitive and therefore well compressible
using dictionary-based methods such as DAGs or grammars. In the context of
selectivity estimation, grammar-compressed trees were used before as synopsis
for structural XPath queries. Here a fully-fledged index over such grammars is
presented. The index allows to execute arbitrary tree algorithms with a
slow-down that is comparable to the space improvement. More interestingly,
certain algorithms execute much faster over the index (because no decompression
occurs). E.g., for structural XPath count queries, evaluating over the index is
faster than previous XPath implementations, often by two orders of magnitude.
The index also allows to serialize XML results (including texts) faster than
previous systems, by a factor of ca. 2-3. This is due to efficient copy
handling of grammar repetitions, and because materialization is totally
avoided. In order to compare with twig join implementations, we implemented a
materializer which writes out pre-order numbers of result nodes, and show its
competitiveness.Comment: 13 page
Linear Compressed Pattern Matching for Polynomial Rewriting (Extended Abstract)
This paper is an extended abstract of an analysis of term rewriting where the
terms in the rewrite rules as well as the term to be rewritten are compressed
by a singleton tree grammar (STG). This form of compression is more general
than node sharing or representing terms as dags since also partial trees
(contexts) can be shared in the compression. In the first part efficient but
complex algorithms for detecting applicability of a rewrite rule under
STG-compression are constructed and analyzed. The second part applies these
results to term rewriting sequences.
The main result for submatching is that finding a redex of a left-linear rule
can be performed in polynomial time under STG-compression.
The main implications for rewriting and (single-position or parallel)
rewriting steps are: (i) under STG-compression, n rewriting steps can be
performed in nondeterministic polynomial time. (ii) under STG-compression and
for left-linear rewrite rules a sequence of n rewriting steps can be performed
in polynomial time, and (iii) for compressed rewrite rules where the left hand
sides are either DAG-compressed or ground and STG-compressed, and an
STG-compressed target term, n rewriting steps can be performed in polynomial
time.Comment: In Proceedings TERMGRAPH 2013, arXiv:1302.599
Improved ESP-index: a practical self-index for highly repetitive texts
While several self-indexes for highly repetitive texts exist, developing a
practical self-index applicable to real world repetitive texts remains a
challenge. ESP-index is a grammar-based self-index on the notion of
edit-sensitive parsing (ESP), an efficient parsing algorithm that guarantees
upper bounds of parsing discrepancies between different appearances of the same
subtexts in a text. Although ESP-index performs efficient top-down searches of
query texts, it has a serious issue on binary searches for finding appearances
of variables for a query text, which resulted in slowing down the query
searches. We present an improved ESP-index (ESP-index-I) by leveraging the idea
behind succinct data structures for large alphabets. While ESP-index-I keeps
the same types of efficiencies as ESP-index about the top-down searches, it
avoid the binary searches using fast rank/select operations. We experimentally
test ESP-index-I on the ability to search query texts and extract subtexts from
real world repetitive texts on a large-scale, and we show that ESP-index-I
performs better that other possible approaches.Comment: This is the full version of a proceeding accepted to the 11th
International Symposium on Experimental Algorithms (SEA2014
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