AMaχoS—Abstract Machine for Xcerpt

Web query languages promise convenient and efficient access to Web data such as XML, RDF, or Topic Maps. Xcerpt is one such Web query language with strong emphasis on novel high-level constructs for effective and convenient query authoring, particularly tailored to versatile access to data in different Web formats such as XML or RDF. However, so far it lacks an efficient implementation to supplement the convenient language features. AMaχoS is an abstract machine implementation for Xcerpt that aims at efficiency and ease of deployment. It strictly separates compilation and execution of queries: Queries are compiled once to abstract machine code that consists in (1) a code segment with instructions for evaluating each rule and (2) a hint segment that provides the abstract machine with optimization hints derived by the query compilation. This article summarizes the motivation and principles behind AMaχoS and discusses how its current architecture realizes these principles

Designing a resource-efficient data structure for mobile data systems

Designing data structures for use in mobile devices requires attention on optimising data volumes with associated benefits for data transmission, storage space and battery use. For semi-structured data, tree summarisation techniques can be used to reduce the volume of structured elements while dictionary compression can efficiently deal with value-based predicates. This project seeks to investigate and evaluate an integration of the two approaches. The key strength of this technique is that both structural and value predicates could be resolved within one graph while further allowing for compression of the resulting data structure. As the current trend is towards the requirement for working with larger semi-structured data sets this work would allow for the utilisation of much larger data sets whilst reducing requirements on bandwidth and minimising the memory necessary both for the storage and querying of the data

A Modular and Flexible Architecture for an Integrated Corpus Query System

The paper describes the architecture of an integrated and extensible corpus query system developed at the University of Stuttgart and gives examples of some of the modules realized within this architecture. The modules form the core of a corpus workbench. Within the proposed architecture, information required for the evaluation of queries may be derived from different knowledge sources (the corpus text, databases, on-line thesauri) and by different means: either through direct lookup in a database or by calling external tools which may infer the necessary information at the time of query evaluation. The information available and the method of information access can be stated declaratively and individually for each corpus, leading to a flexible, extensible and modular corpus workbench.Comment: 10 pages, uuencoded gzip'ped PostScript; presented at COMPLEX'9

The State-of-the-arts in Focused Search

The continuous influx of various text data on the Web requires search engines to improve their retrieval abilities for more specific information. The need for relevant results to a user’s topic of interest has gone beyond search for domain or type specific documents to more focused result (e.g. document fragments or answers to a query). The introduction of XML provides a format standard for data representation, storage, and exchange. It helps focused search to be carried out at different granularities of a structured document with XML markups. This report aims at reviewing the state-of-the-arts in focused search, particularly techniques for topic-specific document retrieval, passage retrieval, XML retrieval, and entity ranking. It is concluded with highlight of open problems

MonetDB/XQuery: a fast XQuery processor powered by a relational engine

Relational XQuery systems try to re-use mature relational data management infrastructures to create fast and scalable XML database technology. This paper describes the main features, key contributions, and lessons learned while implementing such a system. Its architecture consists of (i) a range-based encoding of XML documents into relational tables, (ii) a compilation technique that translates XQuery into a basic relational algebra, (iii) a restricted (order) property-aware peephole relational query optimization strategy, and (iv) a mapping from XML update statements into relational updates. Thus, this system implements all essential XML database functionalities (rather than a single feature) such that we can learn from the full consequences of our architectural decisions. While implementing this system, we had to extend the state-of-the-art with a number of new technical contributions, such as loop-lifted staircase join and efficient relational query evaluation strategies for XQuery theta-joins with existential semantics. These contributions as well as the architectural lessons learned are also deemed valuable for other relational back-end engines. The performance and scalability of the resulting system is evaluated on the XMark benchmark up to data sizes of 11GB. The performance section also provides an extensive benchmark comparison of all major XMark results published previously, which confirm that the goal of purely relational XQuery processing, namely speed and scalability, was met