This thesis reports an ontology-based approach to automatic generation of highly tailored GUI components that can make customized data requests for the end users. Using this GUI generator, without knowing any programming skill a domain expert can browse the data schema through the ontology file of his/her own field, choose attribute fields according to business's needs, and make a highly customized GUI for end users' data requests input. The interface for the domain expert is a tree view structure that shows not only the domain taxonomy categories but also the relationships between classes. By clicking the checkbox associated with each class, the expert indicates his/her choice of the needed information. These choices are stored in a metadata document in XML. From the viewpoint of programmers, the metadata contains no ambiguity; every class in an ontology is unique. The utilizations of the metadata can be various; I have carried out the process of GUI generation. Since every class and every attribute in the class has been formally specified in the ontology, generating GUI is automatic. This approach has been applied to a use case scenario in meteorological and oceanographic (METOC) area. The resulting features of this prototype have been reported in this thesis.

Nowadays many scientific experiment results involve multi-dimensional arrays. It is desirable to store these results in a persistent way and make queries against not only well-structured data objects like arrays but also the metadata that describe the experiments. SPARQL is a Semantic Web standard query language for data and metadata stored in terms of RDF. SciSPARQL is an extended version of SPARQL designed for scientific applications. It includes numeric multi-dimensional array operations and user-defined functions. The SciSPARQL Database Manager (SSDM) is a query processing engine for SciSPARQL. MATLAB is a popular and powerful scientific computing application programming language. We implemented an interface between MATLAB and SciSPARQL called MATLAB SciSPARQL Link (MSL). MSL provides SciSPARQL queries in MATLAB through a client/server interface. It optionally also provides an interface to enable calls to MATLAB in SciSPARQL queries.  With MSL MATLAB users can populate, update, and query SSDM databases it in terms of SciSPARQL queries. For the implementation we use C interfaces of MATLAB and SSDM, and the networking capabilities of SSDM. The DLL we made extends MATLAB with MSL interface functions.

Thesis (M.S. in computer science)--Washington State University, May 2010. / Title from PDF title page (viewed on July 12, 2010). "School of Engineering and Computer Science." Includes bibliographical references (p. 33-34).

Updates in RDF stores have recently been standardised in the SPARQL 1.1 Update specification. However, computing answers entailed by ontologies in triple stores is usually treated orthogonal to updates. Even the W3C's recent SPARQL 1.1 Update language and SPARQL 1.1 Entailment Regimes specifications explicitly exclude a standard behaviour how SPARQL endpoints should treat entailment regimes other than simple entailment in the context of updates. In this paper, we take a first step to close this gap. We define a fragment of SPARQL basic graph patterns corresponding to (the RDFS fragment of) DL-Lite and the corresponding SPARQL update language, dealing with updates both of ABox and of TBox statements. We discuss possible semantics along with potential strategies for implementing them. We treat both, (i) materialised RDF stores, which store all entailed triples explicitly, and (ii) reduced RDF Stores, that is, redundancy-free RDF stores that do not store any RDF triples (corresponding to DL-Lite ABox statements) entailed by others already. / Series: Working Papers on Information Systems, Information Business and Operations

The publication and interchange of RDF datasets online has experienced significant growth in recent years, promoted by different but complementary efforts, such as Linked Open Data, the Web of Things and RDF stream processing systems. However, the current Linked Data infrastructure does not cater for the storage and exchange of sensitive or private data. On the one hand, data publishers need means to limit access to confidential data (e.g. health, financial, personal, or other sensitive data). On the other hand, the infrastructure needs to compress RDF graphs in a manner that minimises the amount of data that is both stored and transferred over the wire. In this paper, we demonstrate how HDT - a compressed serialization format for RDF - can be extended to cater for supporting encryption. We propose a number of different graph partitioning strategies and discuss the benefits and tradeoffs of each approach.

In the last decade, the Resource Description Framework (RDF) has become the de-facto standard for publishing semantic data on the Web. This steady adoption has led to a significant increase in the number and volume of available RDF datasets, exceeding the capabilities of traditional RDF stores. This scenario has introduced severe big semantic data challenges when it comes to managing and querying RDF data at Web scale. Despite the existence of various off-the-shelf Big Data platforms, processing RDF in a distributed environment remains a significant challenge. In this position paper, based on an indepth analysis of the state of the art, we propose to manage large RDF datasets in Flink, a well-known scalable distributed Big Data processing framework. Our approach, which we refer to as FLINKer extends the native graph abstraction of Flink, called Gelly, with RDF graph and SPARQL query processing capabilities.

Made available in DSpace on 2014-06-12T15:58:48Z (GMT). No. of bitstreams: 2 arquivo4701_1.pdf: 1621449 bytes, checksum: 6b5c6ef55f58ad1d27736c6c74fd4ca4 (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2003 / Nos últimos anos, com o crescimento dos sistemas de informação, metadados tornaram-se peças chave no gerenciamento de todo o ciclo de vida desses sistemas. Muitos esforços recentes, tanto das áreas acadêmicas quanto da indústria, estão sendo concentrados em pesquisas relacionadas a metadados. Esses estudos tentam definir metodologias e padrões para construção e interoperabilidade de sistemas de informação baseados em metadados. Atualmente, metadados são utilizados em diversas áreas, como Data Warehouse, bibliotecas eletrônicas, engenharia de software e integração de aplicações heterogêneas. Com o advento da internet, novas aplicações surgem, juntamente com novos padrões para representação. Esses padrões incluem XML (Extensible Markup Language), DTD (Document Type Definition), XML Schema, RDF (Resource Description Framework), RDF Schema, XSLT (Extensible Stylesheet Language Transformation), entre outros. Cada padrão foi desenvolvido para determinados tipos de aplicação. Por exemplo, DTD e XML Schema são padrões para descrição de estruturas de dados, RDF é utilizado para descrição de recursos como páginas Web enquanto que XSLT é utilizado na transformação de documentos XML. A especificação de metadados, aliada à sua gerência eficiente, são os grandes desafios de um ambiente de construção de um Data warehouse. Este trabalho tem como objetivo a construção de uma solução de metadados para o ambiente REDIRIS (Research Environment on Data Integration, Reuse and Quality in Information Systems). O REDIRIS propõe auxiliar os usuários nas tarefas de análise, modelagem, construção e reuso de solução de Data warehouse. O módulo de metadados será responsável por gerenciar toda a informação necessária para a funcionalidade e gerenciamento do ambiente. Para isto, foram projetados e implementados vários metamodelos baseados nos padrões, citados anteriormente. Foi utilizado o MOF (Meta Object Facility) para a construção dos metamodelos e JMI (Java Metadata Interface) para a implementação dos mesmos. As vantagens deste tipo de abordagem são um conjunto de interfaces comuns para gerenciamento dos metadados representados em qualquer padrão (XML, DTD, RDF) e um formato único para intercâmbio desses metadados, o XMI (XML Metadata Interchange)

No description available.

Linked data are usually visualized as graph structures which are useful for browsing resources but less useful for viewing structured data. This thesis proposes a solution how to visualize linked data in a tabular structure. The goal of the visualization is to make an orientation in data easier for a user. A tabular structure for specific data can be designed and managed in a graphic editor. Visualized data can be checked for errors and can be edited to obtain a script which fixes them. Powered by TCPDF (www.tcpdf.org)

The principle of Open Data and Linked data is in growing interest of many organizations, developers and even government institutions. This work is aimed on providing actual information about development of Open and Linked data, further there are introduced featured tools for creating, manipulating, transformation and other operations regarding Open and Linked Data. Finally, there is description of development of Linked Data application based on universal visualization system Payola.