Public government data refers to documents and proceedings which are freely available and accessible. Repositories facilitate the collection, publishing and distribution of data in a centralized and possibly standardized way. Metadata is used to catalog and organize the provided data. The operationality and interoperability depends on the metadata quality. In order to measure the efficiency of a repository the metadata quality needs to be quantified. Quality assessment is considered to be most reliable when carried out by a human expert. This approach, however, is not always feasible. Hence, an automatic assessment of the quality of metadata should be pursued.Proposed metrics from the field of metadata quality assessment are taken, implemented and applied to three public government data repositories, namely GovData.de (Germany), data.gov.uk (United Kingdom) and publicdata.eu (Europe). Five quality metrics were applied: completeness, weighted completeness, accuracy, richness of information and accessibility. The metrics and their implementation will be discussed in detail and the results evaluated.
This chapter covers the scientific background for the Service Level Module of the Unified Service Description Language (USDL). In addition to general service level concepts, we expand on two specific service level fields: security and trust. For that end we first review the state of the art in service level modeling, then we explain the design of the Service Level Module and position it among the rest of USDL. For security, two possible perspectives, a high level business view and a low level engineering approach, are introduced. With regards to trust, USDL is suitable to specify how a service can be rated by its consumers and to ensure that ratings of competing services are comparable, and hence to determine trustworthiness. Additionally, we present a description of non-security-related elements that can be exploited for trust estimation.
We present a mechanism for reliable multicast based on autonomic principles (AutoRM). So-called Beamon nodes exchange information in a peer-to-peer manner, deriving a subjective view of the environment. Applications connect to a Beamon network to participate in reliable communication within groups they declare to be joined to. This short paper describes the general AutoRM concepts, the architecture and protocols used between application and Beamon, as well as between the nodes themselves.
Modelling system behaviour by means of UML Behavioral State Machines is an established practice in software engineering. Usually, code generation is employed to create a system's software components. Although this approach yields software with a good runtime performance, the resulting system behaviour is static. Changes to the behaviour model necessarily provoke an iteration in the code generation workflow and a re-deployment of the generated artefacts. In the area of autonomic systems engineering, it is assumed that systems are able to adapt their runtime behaviour in response to a changing context. Thus, the constraints imposed by a code generation approach make runtime adaptation difficult, if not impossible. This article investigates a solution to this problem by employing interpretation techniques for the runtime execution of UML State Machines, enabling the adaptability of a system's runtime behaviour on the level of single model elements. This is done by devising concepts for behaviour model interpretation, which are then used in a proof-of-concept implementation to demonstrate the feasibility of the approach. For a quantitative evaluation we provide a performance comparison between the proof-of-concept implementation and generated code for a number of benchmark models. We find that UML State Machine interpretation has a performance overhead when compared with static code generation, but found it to be adequate for the majority of situations, except when dealing with high-throughput or delay-sensitive data
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