213 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
The Equivalence Problem for Deterministic MSO Tree Transducers is Decidable
It is decidable for deterministic MSO definable graph-to-string or
graph-to-tree transducers whether they are equivalent on a context-free set of
graphs
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
Deciding Linear Height and Linear Size-to-Height Increase for Macro Tree Transducers
In this paper we study Macro Tree Transducers (MTT), specifically the Linear
Height Increase ("LHI") and Linear input Size to output Height ("LSHI")
constraints. In order to decide whether a Macro tree transducer (MTT) is of LHI
or LSHI, we define a notion of depth-properness: a MTT is depth-proper if, for
each state, there is no bound to the depth at which it places its argument
trees. We show how to effectively put a MTT in depth-proper form. For MTTs in
Depth-proper form, we characterize the LSH property as equivalent to the
finite-nesting property, and we characterize the LHI property as equivalent to
the finiteness of a new type of nesting which we call Multi-Leaf-nesting (or
ML-nesting). As opposed to regular nesting where we look at the nesting of
states applied to a single input node, we count the nesting of states applied
to nodes that are not ancestors of each other. We use this characterization to
give a decision procedure for the LSHI and LHI properties. Finally we consider
the decision problem of the LSOI (Linear input Size to number of distinct
Output subtrees Increase) property. A long standing open problem is whether MTT
of LSOI are as expressive as Attribute Tree Transducers (ATT), in this paper we
show that deciding whether a MTT is of LSOI is as hard as deciding the
equivalence of ATTs
Equivalence Problems for Tree Transducers: A Brief Survey
The decidability of equivalence for three important classes of tree
transducers is discussed. Each class can be obtained as a natural restriction
of deterministic macro tree transducers (MTTs): (1) no context parameters,
i.e., top-down tree transducers, (2) linear size increase, i.e., MSO definable
tree transducers, and (3) monadic input and output ranked alphabets. For the
full class of MTTs, decidability of equivalence remains a long-standing open
problem.Comment: In Proceedings AFL 2014, arXiv:1405.527
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Modeling and Analyzing Systemic Risk in Complex Sociotechnical Systems The Role of Teleology, Feedback, and Emergence
Recent systemic failures such as the BP Deepwater Horizon Oil Spill, Global Financial Crisis, and Northeast Blackout have reminded us, once again, of the fragility of complex sociotechnical systems. Although the failures occurred in very different domains and were triggered by different events, there are, however, certain common underlying mechanisms of abnormalities driving these systemic failures. Understanding these mechanisms is essential to avoid such disasters in the future. Moreover, these disasters happened in sociotechnical systems, where both social and technical elements can interact with each other and with the environment. The nonlinear interactions among these components can lead to an âemergentâ behavior â i.e., the behavior of the whole is more than the sum of its parts â that can be difficult to anticipate and control. Abnormalities can propagate through the systems to cause systemic failures. To ensure the safe operation and production of such complex systems, we need to understand and model the associated systemic risk.
Traditional emphasis of chemical engineering risk modeling is on the technical components of a chemical plant, such as equipment and processes. However, a chemical plant is more than a set of equipment and processes, with the human elements playing a critical role in decision-making. Industrial statistics show that about 70% of the accidents are caused by human errors. So, new modeling techniques that go beyond the classical equipment/process-oriented approaches to include the human elements (i.e., the âsocioâ part of the sociotechnical systems) are needed for analyzing systemic risk of complex sociotechnical systems. This thesis presents such an approach.
This thesis presents a new knowledge modeling paradigm for systemic risk analysis that goes beyond chemical plants by unifying different perspectives. First, we develop a unifying teleological, control theoretic framework to model decision-making knowledge in a complex system. The framework allows us to identify systematically the common failure mechanisms behind systemic failures in different domains. We show how cause-and-effect knowledge can be incorporated into this framework by using signed directed graphs. We also develop an ontology-driven knowledge modeling component and show how this can support decision-making by using a case study in public health emergency. This is the first such attempt to develop an ontology for public health documents. Lastly, from a control-theoretic perspective, we address the question, âhow do simple individual components of a system interact to produce a system behavior that cannot be explained by the behavior of just the individual components alone?â Through this effort, we attempt to bridge the knowledge gap between control theory and complexity science
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