XGL: a graphical query language for XML

In this paper we present a graphical query language for XML. The language, based on a simple form of graph grammars, permits us to extract data and reorganize information in a new structure. As with most of the current query languages for XML, queries consist of two parts: one extracting a subgraph and one constructing the output graph. The semantics of queries is given in terms of graph grammars. The use of graph grammars makes it possible to define, in a simple way, the structural properties of both the subgraph that has to be extracted and the graph that has to be constructed. We provide an example-driven comparison of our language w.r.t. other XML query languages, and show the effectiveness and simplicity of our approach.

A query language for xml should be flexible enough to cover the whole range of information sources that can be labelled by xml, including databases and web documents. In this article we present a comparative analysis of several query languages that have been created for xml. We study sets of desirable traits both from the point of view of the semistructured data community (that emphasise especially very large databases, the integration of heterogeneous sources and data transformation into common interchange formats), and from the point of view of the information retrieval community (that emphasise especially full-text searches, manipulation of the sets of results, inclusion relationships, distances, and ranking of the result documents).

The vast quantity of scientific data produced in life sciences demands the use of sophisticated storage and analysis techniques. In particular, for biological pathways graph analysis plays an important role and data is commonly available in XML-based formats. Thus, there is a growing need to make analysis capabilities available through query languages for XML. This paper presents an approach to extend XQuery for graph analysis with focus on data for biological pathways. A graph model is introduced within the XQuery environment. New built-in functions define the available operations on the graph model. XQuery expressions can be utilized to populate graphs with data and execute graph algorithms. Graph data and results of algorithms can be accessed in an XML representation for further processing. In addition, a reference mechanism can be used to preserve associations from graph data to the original XML data. The approach has been implemented as an extension to exist. First evaluations of the implementation show that the introduced approach is practical and efficient for reaction networks with several thousand vertices and edges.

XML and web services are set to revolutionise the Internet. However there is currently an impedance mismatch between XML and traditional programming languages such as Java and C#. This can lead to difficulties implementing web services using these languages. In this paper we discuss this issue and investigate a particular solution, using the XML query language, XQuery, to implement web services. This leads to a system comprising distributed XML objects.

Semantic web technologies promise to ease the pain of data and system integration in the life sciences. The semantic web consists of standards such as XML for mark-up of contents, RDF for representation of triplets, and OWL to define ontologies. We discuss three approaches for querying semantic web contents and building integrated bioinformatics applications, which allows bioinformaticians to make an informed choice for their data integration needs. Besides already established approach such as XQuery, we compare two novel rule-based approaches, namely Xcerpt - a versatile XML and RDF query language, and Prova - a language for rule-based Java scripting. We demonstrate the core features and limitations of these three approaches through a case study, which comprises an ontology browser, which supports retrieval of protein structure and sequence information for proteins annotated with terms from the ontology.

The Web so far has been incredibly successful at delivering information to human users. So successful actually, that there is now an urgent need to go beyond a browsing human and make information accessible to applications, in order to offer automation, inter-operation and Web-awareness among services. To do so, information from Web sources needs to be accessible in a structured way. XML and its various extensions (data-models, query languages) are a step in this direction. Unfortunately, the Web is not yet a well organized repository of nicely structured XML documents but rather a conglomerate of volatile HTML pages, for which structure has to be extracted. To address this problem, we present the World Wide Web Wrapper Factory (W4F), a Java toolkit for the generation of wrappers for Web sources. Our main contributions are: (1) an expressive language to specify the extraction of complex structures from HTML pages; (2) a declarative mapping to XML documents, with the automatic generation of the corresponding DTDs; (3) some visual supports to make the engineering of wrappers faster and easier As an illustration, we show how we can, via W4F intermediation, transparently query HTML sources from an XML query language.

We define a language G for querying data represented as a labeled graph G. By considering G as a relation, this graphical query language can be viewed as a relational query language, and its expressive power can be compared to that of other relational query languages. We do not propose G as an alternative to general purpose relational query languages, but rather as a complementary language in which recursive queries are simple to formulate. The user is aided in this formulation by means of a graphical interface. The provision of regular expressions in G allows recursive queries more general than transitive closure to be posed, although the language is not as powerful as those based on function-free Horn clauses. However, we hope to be able to exploit well-known graph algorithms in evaluating recursive queries efficiently, a topic which has received widespread attention recently.

VISUAL is a graphical icon-based query language designed for scientific databases where visualization of the relationships are important for the domain scientist to express queries. Graphical objects are not tied to the underlying formalism; instead, they represent the relationships of the application domain. VISUAL supports relational, nested, and object-oriented models naturally and has formal basis. In addition to set and bag constructs for complex objects, sequences are also supported by the data model. Concepts of external and internal queries are developed as modularization tools. A new parallel/distributed query processing paradigm is presented. VISUAL query processing techniques are also discussed.

This paper investigates the expressive power of DATALOG¬ queries under unique T-stable model semantics, i.e., a query on a given database yields an answer if and only if there exists a unique T-stable model. Under this semantics DATALOG¬ queries are shown to express exactly all decision problems with unique solutions. Obviously, unique T-stable model semantics is the ‘natural’ semantics for queries with at most one T-stable model or with exactly one T-stable model for every database. The expressive powers of of these two classes of queries are investigated as well but it turns out that any practical language for such queries cannot get to an expressive power higher than DATALOG with stratified negation.

It has become desirable to provide a way of keyword search for users to query structured information in an XML database (data-centric retrieval) by combining database and information retrieval techniques. Therefore, the key challenges of keyword search in the XML database are how to define appropriate result models meeting user’s search intents, how to search the results by using efficient algorithms, and how to ranking the results. In this chapter, on one hand, the authors present the foundational knowledge of XML keyword search such as XML data models, XML query languages, inverted index, and Dewey encoding. On the other hand, some existing typical researches of keyword search in XML are presented, including the results models such as Smallest Lowest Common Ancestor (SLCA), Exclusive Lowest Common Ancestor (ELCA), Meaningful Lowest Common Ancestor (MLCA), the related search algorithms, and the ranking approaches.

No abstract available.

The Complexity of the Kn,n-Problem for Node Replacement Graph Languages

In this chapter, the research background is discussed. This includes XML model, XML query languages, XML schema languages, XML Application Program Interface, XML documents types, XML data storage approaches, relational database model, and the similarities and differences between XML model and relational database model. Finally the chapter summary is given.

Data cubes for OLAP (On‐Line Analytical Processing) often need to be constructed from data located in several distributed and autonomous information sources. Such a data integration process is challenging due to semantic, syntactic, and structural heterogeneity among the data. While XML (extensible markup language) is the de facto standard for data exchange, the three types of heterogeneity remain. Moreover, popular path‐oriented XML query languages, such as XQuery, require the user to know in much detail the structure of the documents to be processed and are, thus, effectively impractical in many real‐world data integration tasks. Several Lowest Common Ancestor (LCA)‐based XML query evaluation strategies have recently been introduced to provide a more structure‐independent way to access XML documents. We shall, however, show that this approach leads in the context of certain—not uncommon—types of XML documents to undesirable results. This article introduces a novel high‐level data extraction primitive that utilizes the purpose‐built Smallest Possible Context (SPC) query evaluation strategy. We demonstrate, through a system prototype for OLAP data cube construction and a sample application in informetrics, that our approach has real advantages in data integration.

A formal model for an expressive fragment of XSLT