26,165 research outputs found

    Metamodel Instance Generation: A systematic literature review

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    Modelling and thus metamodelling have become increasingly important in Software Engineering through the use of Model Driven Engineering. In this paper we present a systematic literature review of instance generation techniques for metamodels, i.e. the process of automatically generating models from a given metamodel. We start by presenting a set of research questions that our review is intended to answer. We then identify the main topics that are related to metamodel instance generation techniques, and use these to initiate our literature search. This search resulted in the identification of 34 key papers in the area, and each of these is reviewed here and discussed in detail. The outcome is that we are able to identify a knowledge gap in this field, and we offer suggestions as to some potential directions for future research.Comment: 25 page

    Modeling views in the layered view model for XML using UML

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    In data engineering, view formalisms are used to provide flexibility to users and user applications by allowing them to extract and elaborate data from the stored data sources. Conversely, since the introduction of Extensible Markup Language (XML), it is fast emerging as the dominant standard for storing, describing, and interchanging data among various web and heterogeneous data sources. In combination with XML Schema, XML provides rich facilities for defining and constraining user-defined data semantics and properties, a feature that is unique to XML. In this context, it is interesting to investigate traditional database features, such as view models and view design techniques for XML. However, traditional view formalisms are strongly coupled to the data language and its syntax, thus it proves to be a difficult task to support views in the case of semi-structured data models. Therefore, in this paper we propose a Layered View Model (LVM) for XML with conceptual and schemata extensions. Here our work is three-fold; first we propose an approach to separate the implementation and conceptual aspects of the views that provides a clear separation of concerns, thus, allowing analysis and design of views to be separated from their implementation. Secondly, we define representations to express and construct these views at the conceptual level. Thirdly, we define a view transformation methodology for XML views in the LVM, which carries out automated transformation to a view schema and a view query expression in an appropriate query language. Also, to validate and apply the LVM concepts, methods and transformations developed, we propose a view-driven application development framework with the flexibility to develop web and database applications for XML, at varying levels of abstraction

    Generating a contract checker for an SLA language

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    SLAng is a language for expressing Service LevelAgreements (SLAs) under development as part of the Europeanproject TAPAS. It is defined using a meta-model, an instance ofthe Meta-Object Facility (MOF) model, in which the relationshipbetween the syntax of the language and its domain of applicationis explicitly represented, and the violation semantics ofthe language defined using Object Constraint Language (OCL)constraints. The concrete syntax of the language is the XMLMeta-data Interchange (XMI) mapping of the syntactic part ofthe meta-model. In this paper we describe how the Java MetadataInterface (JMI) mapping can be applied to the meta-modelof the language to generate interfaces and classes to create andquery SLAs and relevant service monitoring data in memory;and how an OCL interpreter can be applied to check violationconstraints over this data, resulting in the implementation of acontract checker that is highly likely to respect the semantics ofthe language

    Visual exploration and retrieval of XML document collections with the generic system X2

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    This article reports on the XML retrieval system X2 which has been developed at the University of Munich over the last five years. In a typical session with X2, the user first browses a structural summary of the XML database in order to select interesting elements and keywords occurring in documents. Using this intermediate result, queries combining structure and textual references are composed semiautomatically. After query evaluation, the full set of answers is presented in a visual and structured way. X2 largely exploits the structure found in documents, queries and answers to enable new interactive visualization and exploration techniques that support mixed IR and database-oriented querying, thus bridging the gap between these three views on the data to be retrieved. Another salient characteristic of X2 which distinguishes it from other visual query systems for XML is that it supports various degrees of detailedness in the presentation of answers, as well as techniques for dynamically reordering and grouping retrieved elements once the complete answer set has been computed

    How to cite curated databases and how to make them citable

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    Generating Concise and Readable Summaries of XML Documents

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    XML has become the de-facto standard for data representation and exchange, resulting in large scale repositories and warehouses of XML data. In order for users to understand and explore these large collections, a summarized, bird's eye view of the available data is a necessity. In this paper, we are interested in semantic XML document summaries which present the "important" information available in an XML document to the user. In the best case, such a summary is a concise replacement for the original document itself. At the other extreme, it should at least help the user make an informed choice as to the relevance of the document to his needs. In this paper, we address the two main issues which arise in producing such meaningful and concise summaries: i) which tags or text units are important and should be included in the summary, ii) how to generate summaries of different sizes.%for different memory budgets. We conduct user studies with different real-life datasets and show that our methods are useful and effective in practice
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