Open Access-Publikationen

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https://www.weizenbaum-library.de/handle/id/23

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Auflistung Open Access-Publikationen nach Forschungsgruppen "Digitalisierung der Wissenschaft"

Gerade angezeigt 1 - 6 von 6
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    IDS as a Foundation for Open Data Ecosystems
    (Springer International Publishing, 2022) Kirstein, Fabian; Bohlen, Vincent; Otto, Boris; Ten Hompel, Michael; Wrobel, Stefan
    Open data is a popular and flourishing concept. The availability of open and structured data is the foundation of new business models, citizen engagement, and scientific research. However, open data still faces many issues to unfold its full potential, including usability, quality, legal, privacy, strategic, and technical barriers. In addition, the public sector remains its main provider, while industry stakeholders are still reluctant to participate in open data ecosystems. In this article, we present an architecture to overcome these drawbacks by utilizing the concepts, specifications, and technologies provided by International Data Spaces. We developed a prototype to demonstrate and evaluate the practical adoption of our architecture. Our work shows that IDS can act a vital foundation for open data ecosystems. The presented solution is available as open source software.
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    Mythos Blockchain: Zwischen Hoffnung und Realität
    (Fraunhofer-Institut für Offene Kommunikationssysteme FOKUS, 2021) Kirstein, Fabian; Altenbernd, Anton; Lämmel, Philipp
    Vertrauen schaffen, vermittelnde Institutionen oder auch gleich den Staat ersetzen und das Internet revolutionieren – die mit dem »Mythos Blockchain« verbundenen Ideen schienen keine Grenzen zu kennen. Gut vier Jahre nach dem ersten ÖFIT-White Paper zum Thema wird es Zeit für eine Bestandsaufnahme, was von den Ideen übriggeblieben ist. Zusammen mit unseren Kollegen vom Weizenbaum-Institut geben wir einen Überblick über die Entwicklungen der letzten Jahre. Ein gemeinsames Verständnis vom Stand der Technologie ist wichtig, um die beispielhaften Anwendungsfälle analysieren zu können. Daraus werden Schlüsselfaktoren kristallisiert und Handlungsempfehlungen für den zukünftigen Einsatz der vielfältigen Technologiebausteine abgeleitet.
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    Provenance Management over Linked Data Streams
    (2019) Liu, Qian; Wylot, Marcin; Le Phuoc, Danh; Hauswirth, Manfred
    Provenance describes how results are produced starting from data sources, curation, recovery, intermediate processing, to the final results. Provenance has been applied to solve many problems and in particular to understand how errors are propagated in large-scale environments such as Internet of Things, Smart Cities. In fact, in such environments operations on data are often performed by multiple uncoordinated parties, each potentially introducing or propagating errors. These errors cause uncertainty of the overall data analytics process that is further amplified when many data sources are combined and errors get propagated across multiple parties. The ability to properly identify how such errors influence the results is crucial to assess the quality of the results. This problem becomes even more challenging in the case of Linked Data Streams, where data is dynamic and often incomplete. In this paper, we introduce methods to compute provenance over Linked Data Streams. More specifically, we propose provenance management techniques to compute provenance of continuous queries executed over complete Linked Data streams. Unlike traditional provenance management techniques, which are applied on static data, we focus strictly on the dynamicity and heterogeneity of Linked Data streams. Specifically, in this paper we describe: i) means to deliver a dynamic provenance trace of the results to the user, ii) a system capable to execute queries over dynamic Linked Data and compute provenance of these queries, and iii) an empirical evaluation of our approach using real-world datasets.
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    Pushing the Scalability of RDF Engines on IoT Edge Devices
    (2020) Le-Tuan, Anh; Hayes, Conor; Hauswirth, Manfred; Le-Phuoc, Danh
    Semantic interoperability for the Internet of Things (IoT) is enabled by standards and technologies from the Semantic Web. As recent research suggests a move towards decentralised IoT architectures, we have investigated the scalability and robustness of RDF (Resource Description Framework)engines that can be embedded throughout the architecture, in particular at edge nodes. RDF processing at the edge facilitates the deployment of semantic integration gateways closer to low-level devices. Our focus is on how to enable scalable and robust RDF engines that can operate on lightweight devices. In this paper, we have first carried out an empirical study of the scalability and behaviour of solutions for RDF data management on standard computing hardware that have been ported to run on lightweight devices at the network edge. The findings of our study shows that these RDF store solutions have several shortcomings on commodity ARM (Advanced RISC Machine) boards that are representative of IoT edge node hardware. Consequently, this has inspired us to introduce a lightweight RDF engine, which comprises an RDF storage and a SPARQL processor for lightweight edge devices, called RDF4Led. RDF4Led follows the RISC-style (Reduce Instruction Set Computer) design philosophy. The design constitutes a flash-aware storage structure, an indexing scheme, an alternative buffer management technique and a low-memory-footprint join algorithm that demonstrates improved scalability and robustness over competing solutions. With a significantly smaller memory footprint, we show that RDF4Led can handle 2 to 5 times more data than popular RDF engines such as Jena TDB (Tuple Database) and RDF4J, while consuming the same amount of memory. In particular, RDF4Led requires 10%–30% memory of its competitors to operate on datasets of up to 50 million triples. On memory-constrained ARM boards, it can perform faster updates and can scale better than Jena TDB and Virtuoso. Furthermore, we demonstrate considerably faster query operations than Jena TDB and RDF4J.
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    Surfing in sound: Sonification of hidden web tracking
    (Georgia Institute of Technology, 2019) Lutz, Otto Hans-Martin; Kröger, Jacob; Schneiderbauer, Manuel; Hauswirth, Manfred
    Web tracking is found on 90% of common websites. It allows online behavioral analysis which can reveal insights to sensitive personal data of an individual. Most users are not aware of the amout of web tracking happening in the background. This paper contributes a sonification-based approach to raise user awareness by conveying information on web tracking through sound while the user is browsing the web. We present a framework for live web tracking analysis, conversion to Open Sound Control events and sonification. The amount of web tracking is disclosed by sound each time data is exchanged with a web tracking host. When a connection to one of the most prevalent tracking companies is established, this is additionally indicated by a voice whispering the company name. Compared to existing approaches on web tracking sonification, we add the capability to monitor any network connection, including all browsers, applications and devices. An initial user study with 12 participants showed empirical support for our main hypothesis: exposure to our sonification significantly raises web tracking awareness.
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    The connection of open science practices and the methodological approach of researchers
    (2022) Steinhardt, Isabel; Bauer, Mareike; Wünsche, Hannes; Schimmler, Sonja
    The Open Science movement is gaining tremendous popularity and tries to initiate changes in science, for example the sharing and reuse of data. The new requirements that come with Open Science poses researchers with several challenges. While most of these challenges have already been addressed in several studies, little attention has been paid so far to the underlying Open Science practices (OSP). An exploratory study was conducted focusing on the OSP relating to sharing and using data. 13 researchers from the Weizenbaum Institute were interviewed. The Weizenbaum Institute is an interdisciplinary research institute in Germany that was founded in 2017. To reconstruct OSP a grounded theory methodology (Strauss in Qualitative Analysis for Social Scientists, Cambridge University Press, Cambridge, 1987) was used and classified OSP into open production, open distribution and open consumption (Smith in Openness as social praxis. First Monday, 2017). The research shows that apart from the disciplinary background and research environment, the methodological approach and the type of research data play a major role in the context of OSP. The interviewees’ self-attributions related to the types of data they work with: qualitative, quantitative, social media and source code. With regard to the methodological approach and type of data, it was uncovered that uncertainties and missing knowledge, data protection, competitive disadvantages, vulnerability and costs are the main reasons for the lack of openness. The analyses further revealed that knowledge and established data infrastructures as well as competitive advantages act as drivers for openness. Because of the link between research data and OSP, the authors of this paper argue that in order to promote OSP, the methodological approach and the type of research data must also be considered