10,623 research outputs found
Algorithms and implementation of functional dependency discovery in XML : a thesis presented in partial fulfilment of the requirements for the degree of Master of Information Sciences in Information Systems at Massey University
1.1 Background Following the advent of the web, there has been a great demand for data interchange between applications using internet infrastructure. XML (extensible Markup Language) provides a structured representation of data empowered by broad adoption and easy deployment. As a subset of SGML (Standard Generalized Markup Language), XML has been standardized by the World Wide Web Consortium (W3C) [Bray et al., 2004], XML is becoming the prevalent data exchange format on the World Wide Web and increasingly significant in storing semi-structured data. After its initial release in 1996, it has evolved and been applied extensively in all fields where the exchange of structured documents in electronic form is required. As with the growing popularity of XML, the issue of functional dependency in XML has recently received well deserved attention. The driving force for the study of dependencies in XML is it is as crucial to XML schema design, as to relational database(RDB) design [Abiteboul et al., 1995]
Probabilistic Constraint Logic Programming
This paper addresses two central problems for probabilistic processing
models: parameter estimation from incomplete data and efficient retrieval of
most probable analyses. These questions have been answered satisfactorily only
for probabilistic regular and context-free models. We address these problems
for a more expressive probabilistic constraint logic programming model. We
present a log-linear probability model for probabilistic constraint logic
programming. On top of this model we define an algorithm to estimate the
parameters and to select the properties of log-linear models from incomplete
data. This algorithm is an extension of the improved iterative scaling
algorithm of Della-Pietra, Della-Pietra, and Lafferty (1995). Our algorithm
applies to log-linear models in general and is accompanied with suitable
approximation methods when applied to large data spaces. Furthermore, we
present an approach for searching for most probable analyses of the
probabilistic constraint logic programming model. This method can be applied to
the ambiguity resolution problem in natural language processing applications.Comment: 35 pages, uses sfbart.cl
A Grammatical Inference Approach to Language-Based Anomaly Detection in XML
False-positives are a problem in anomaly-based intrusion detection systems.
To counter this issue, we discuss anomaly detection for the eXtensible Markup
Language (XML) in a language-theoretic view. We argue that many XML-based
attacks target the syntactic level, i.e. the tree structure or element content,
and syntax validation of XML documents reduces the attack surface. XML offers
so-called schemas for validation, but in real world, schemas are often
unavailable, ignored or too general. In this work-in-progress paper we describe
a grammatical inference approach to learn an automaton from example XML
documents for detecting documents with anomalous syntax.
We discuss properties and expressiveness of XML to understand limits of
learnability. Our contributions are an XML Schema compatible lexical datatype
system to abstract content in XML and an algorithm to learn visibly pushdown
automata (VPA) directly from a set of examples. The proposed algorithm does not
require the tree representation of XML, so it can process large documents or
streams. The resulting deterministic VPA then allows stream validation of
documents to recognize deviations in the underlying tree structure or
datatypes.Comment: Paper accepted at First Int. Workshop on Emerging Cyberthreats and
Countermeasures ECTCM 201
Rational stochastic languages
The goal of the present paper is to provide a systematic and comprehensive
study of rational stochastic languages over a semiring K \in {Q, Q +, R, R+}. A
rational stochastic language is a probability distribution over a free monoid
\Sigma^* which is rational over K, that is which can be generated by a
multiplicity automata with parameters in K. We study the relations between the
classes of rational stochastic languages S rat K (\Sigma). We define the notion
of residual of a stochastic language and we use it to investigate properties of
several subclasses of rational stochastic languages. Lastly, we study the
representation of rational stochastic languages by means of multiplicity
automata.Comment: 35 page
Learning probability distributions generated by finite-state machines
We review methods for inference of probability distributions generated by probabilistic automata and related models for sequence generation. We focus on methods that can be proved to learn in the inference
in the limit and PAC formal models. The methods we review are state merging and state splitting methods for probabilistic deterministic automata and the recently developed spectral method for nondeterministic probabilistic automata. In both cases, we derive them from a high-level algorithm described in terms of the Hankel matrix of the distribution to be learned, given as an oracle, and then describe how to adapt that algorithm to account for the error introduced by a finite sample.Peer ReviewedPostprint (author's final draft
Lateral transfer in Stochastic Dollo models
Lateral transfer, a process whereby species exchange evolutionary traits
through non-ancestral relationships, is a frequent source of model
misspecification in phylogenetic inference. Lateral transfer obscures the
phylogenetic signal in the data as the histories of affected traits are mosaics
of the overall phylogeny. We control for the effect of lateral transfer in a
Stochastic Dollo model and a Bayesian setting. Our likelihood is highly
intractable as the parameters are the solution of a sequence of large systems
of differential equations representing the expected evolution of traits along a
tree. We illustrate our method on a data set of lexical traits in Eastern
Polynesian languages and obtain an improved fit over the corresponding model
without lateral transfer.Comment: Improvements suggested by reviewer
DNA ANALYSIS USING GRAMMATICAL INFERENCE
An accurate language definition capable of distinguishing between coding and non-coding DNA has important applications and analytical significance to the field of computational biology. The method proposed here uses positive sample grammatical inference and statistical information to infer languages for coding DNA.
An algorithm is proposed for the searching of an optimal subset of input sequences for the inference of regular grammars by optimizing a relevant accuracy metric. The algorithm does not guarantee the finding of the optimal subset; however, testing shows improvement in accuracy and performance over the basis algorithm.
Testing shows that the accuracy of inferred languages for components of DNA are consistently accurate. By using the proposed algorithm languages are inferred for coding DNA with average conditional probability over 80%. This reveals that languages for components of DNA can be inferred and are useful independent of the process that created them. These languages can then be analyzed or used for other tasks in computational biology.
To illustrate potential applications of regular grammars for DNA components, an inferred language for exon sequences is applied as post processing to Hidden Markov exon prediction to reduce the number of wrong exons detected and improve the specificity of the model significantly
- …