2,527 research outputs found
Hypermedia-based discovery for source selection using low-cost linked data interfaces
Evaluating federated Linked Data queries requires consulting multiple sources on the Web. Before a client can execute queries, it must discover data sources, and determine which ones are relevant. Federated query execution research focuses on the actual execution, while data source discovery is often marginally discussed-even though it has a strong impact on selecting sources that contribute to the query results. Therefore, the authors introduce a discovery approach for Linked Data interfaces based on hypermedia links and controls, and apply it to federated query execution with Triple Pattern Fragments. In addition, the authors identify quantitative metrics to evaluate this discovery approach. This article describes generic evaluation measures and results for their concrete approach. With low-cost data summaries as seed, interfaces to eight large real-world datasets can discover each other within 7 minutes. Hypermedia-based client-side querying shows a promising gain of up to 50% in execution time, but demands algorithms that visit a higher number of interfaces to improve result completeness
Privacy-Preserving Reengineering of Model-View-Controller Application Architectures Using Linked Data
When a legacy systemâs software architecture cannot be redesigned, implementing
additional privacy requirements is often complex, unreliable and
costly to maintain. This paper presents a privacy-by-design approach to
reengineer web applications as linked data-enabled and implement access
control and privacy preservation properties. The method is based on the
knowledge of the application architecture, which for the Web of data is
commonly designed on the basis of a model-view-controller pattern. Whereas
wrapping techniques commonly used to link data of web applications duplicate
the security source code, the new approach allows for the controlled
disclosure of an applicationâs data, while preserving non-functional properties
such as privacy preservation. The solution has been implemented
and compared with existing linked data frameworks in terms of reliability,
maintainability and complexity
BlogForever D2.6: Data Extraction Methodology
This report outlines an inquiry into the area of web data extraction, conducted within the context of blog preservation. The report reviews theoretical advances and practical developments for implementing data extraction. The inquiry is extended through an experiment that demonstrates the effectiveness and feasibility of implementing some of the suggested approaches. More specifically, the report discusses an approach based on unsupervised machine learning that employs the RSS feeds and HTML representations of blogs. It outlines the possibilities of extracting semantics available in blogs and demonstrates the benefits of exploiting available standards such as microformats and microdata. The report proceeds to propose a methodology for extracting and processing blog data to further inform the design and development of the BlogForever platform
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