14,466 research outputs found
XML content warehousing: Improving sociological studies of mailing lists and web data
In this paper, we present the guidelines for an XML-based approach for the
sociological study of Web data such as the analysis of mailing lists or
databases available online. The use of an XML warehouse is a flexible solution
for storing and processing this kind of data. We propose an implemented
solution and show possible applications with our case study of profiles of
experts involved in W3C standard-setting activity. We illustrate the
sociological use of semi-structured databases by presenting our XML Schema for
mailing-list warehousing. An XML Schema allows many adjunctions or crossings of
data sources, without modifying existing data sets, while allowing possible
structural evolution. We also show that the existence of hidden data implies
increased complexity for traditional SQL users. XML content warehousing allows
altogether exhaustive warehousing and recursive queries through contents, with
far less dependence on the initial storage. We finally present the possibility
of exporting the data stored in the warehouse to commonly-used advanced
software devoted to sociological analysis
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
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