16,710 research outputs found
Linear Bounded Composition of Tree-Walking Tree Transducers: Linear Size Increase and Complexity
Compositions of tree-walking tree transducers form a hierarchy with respect
to the number of transducers in the composition. As main technical result it is
proved that any such composition can be realized as a linear bounded
composition, which means that the sizes of the intermediate results can be
chosen to be at most linear in the size of the output tree. This has
consequences for the expressiveness and complexity of the translations in the
hierarchy. First, if the computed translation is a function of linear size
increase, i.e., the size of the output tree is at most linear in the size of
the input tree, then it can be realized by just one, deterministic,
tree-walking tree transducer. For compositions of deterministic transducers it
is decidable whether or not the translation is of linear size increase. Second,
every composition of deterministic transducers can be computed in deterministic
linear time on a RAM and in deterministic linear space on a Turing machine,
measured in the sum of the sizes of the input and output tree. Similarly, every
composition of nondeterministic transducers can be computed in simultaneous
polynomial time and linear space on a nondeterministic Turing machine. Their
output tree languages are deterministic context-sensitive, i.e., can be
recognized in deterministic linear space on a Turing machine. The membership
problem for compositions of nondeterministic translations is nondeterministic
polynomial time and deterministic linear space. The membership problem for the
composition of a nondeterministic and a deterministic tree-walking tree
translation (for a nondeterministic IO macro tree translation) is log-space
reducible to a context-free language, whereas the membership problem for the
composition of a deterministic and a nondeterministic tree-walking tree
translation (for a nondeterministic OI macro tree translation) is possibly
NP-complete
Evaluating the performance of model transformation styles in Maude
Rule-based programming has been shown to be very successful in many application areas. Two prominent examples are the specification of model transformations in model driven development approaches and the definition of structured operational semantics of formal languages. General rewriting frameworks such as Maude are flexible enough to allow the programmer to adopt and mix various rule styles. The choice between styles can be biased by the programmer’s background. For instance, experts in visual formalisms might prefer graph-rewriting styles, while experts in semantics might prefer structurally inductive rules. This paper evaluates the performance of different rule styles on a significant benchmark taken from the literature on model transformation. Depending on the actual transformation being carried out, our results show that different rule styles can offer drastically different performances. We point out the situations from which each rule style benefits to offer a valuable set of hints for choosing one style over the other
The copying power of one-state tree transducers
One-state deterministic top-down tree transducers (or, tree homomorphisms) cannot handle "prime copying," i.e., their class of output (string) languages is not closed under the operation L → {)f(n) w ε L, f(n) ≥ 1}, where f is any integer function whose range contains numbers with arbitrarily large prime factors (such as a polynomial). The exact amount of nonclosure under these copying operations is established for several classes of input (tree) languages. These results are relevant to the extended definable (or, restricted parallel level) languages, to the syntax-directed translation of context-free languages, and to the tree transducer hierarchy.\ud
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XQuery Streaming by Forest Transducers
Streaming of XML transformations is a challenging task and only very few
systems support streaming. Research approaches generally define custom
fragments of XQuery and XPath that are amenable to streaming, and then design
custom algorithms for each fragment. These languages have several shortcomings.
Here we take a more principles approach to the problem of streaming
XQuery-based transformations. We start with an elegant transducer model for
which many static analysis problems are well-understood: the Macro Forest
Transducer (MFT). We show that a large fragment of XQuery can be translated
into MFTs --- indeed, a fragment of XQuery, that can express important features
that are missing from other XQuery stream engines, such as GCX: our fragment of
XQuery supports XPath predicates and let-statements. We then rely on a
streaming execution engine for MFTs, one which uses a well-founded set of
optimizations from functional programming, such as strictness analysis and
deforestation. Our prototype achieves time and memory efficiency comparable to
the fastest known engine for XQuery streaming, GCX. This is surprising because
our engine relies on the OCaml built in garbage collector and does not use any
specialized buffer management, while GCX's efficiency is due to clever and
explicit buffer management.Comment: Full version of the paper in the Proceedings of the 30th IEEE
International Conference on Data Engineering (ICDE 2014
Three hierarchies of transducers
Composition of top-down tree transducers yields a proper hierarchy of transductions and of output languages. The same is true for ETOL systems (viewed as transducers) and for two-way generalized sequential machines
Learning Tree Distributions by Hidden Markov Models
Hidden tree Markov models allow learning distributions for tree structured
data while being interpretable as nondeterministic automata. We provide a
concise summary of the main approaches in literature, focusing in particular on
the causality assumptions introduced by the choice of a specific tree visit
direction. We will then sketch a novel non-parametric generalization of the
bottom-up hidden tree Markov model with its interpretation as a
nondeterministic tree automaton with infinite states.Comment: Accepted in LearnAut2018 worksho
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