THE ROLE OF XML IN THE MODELING PROCESS OF A VIRTUAL BUSINESS

The aim of this paper is to describe the XML stack of languages used in the implementation process of a web application. This application is based on a three tier architecture named XRX. In this type of architecture there is no need for data model transformations between the tiers of the architecture like in the classical architecture. So the applications developed according XRX architecture become more flexible, efficient and simple.XML, XPath, XQuery, XSLT, XForms, XRX, UBL

XMLTK: An XML Toolkit for Scalable XML Stream Processing

We describe a toolkit for highly scalable XML data processing, consisting of two components. The first is a collection of stand-alone XML tools, s.a. sort- ing, aggregation, nesting, and unnesting, that can be chained to express more complex restructurings. The second is a highly scalable XPath processor for XML streams that can be used to develop scalable solutions for XML stream applications. In this paper we dis- cuss the tools, and some of the techniques we used to achieve high scalability. The toolkit is freely available as an open-source project

Staircase Join: Teach a Relational DBMS to Watch its (Axis) Steps

Relational query processors derive much of their effectiveness from the awareness of specific table properties like sort order, size, or absence of duplicate tuples. This text applies (and adapts) this successful principle to database-supported XML and XPath processing: the relational system is made tree aware, i.e., tree properties like subtree size, intersection of paths, inclusion or disjointness of subtrees are made explicit. We propose a local change to the database kernel, the staircase join, which encapsulates the necessary tree knowledge needed to improve XPath performance. Staircase join operates on an XML encoding which makes this knowledge available at the cost of simple integer operations (e.g., +, <=). We finally report on quite promising experiments with a staircase join enhanced main-memory database kernel

On the tree-transformation power of XSLT

XSLT is a standard rule-based programming language for expressing transformations of XML data. The language is currently in transition from version 1.0 to 2.0. In order to understand the computational consequences of this transition, we restrict XSLT to its pure tree-transformation capabilities. Under this focus, we observe that XSLT~1.0 was not yet a computationally complete tree-transformation language: every 1.0 program can be implemented in exponential time. A crucial new feature of version~2.0, however, which allows nodesets over temporary trees, yields completeness. We provide a formal operational semantics for XSLT programs, and establish confluence for this semantics

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