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Auflistung Aufsätze nach Forschungsgruppen "Digitalisierung und vernetzte Sicherheit"

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    How Do Environmental and Methodological Factors Influence Study Participants’ Answers in Surveys on Risk Perception in the Context of Climate Change and Heat Stress?
    (2025) Holzen, Veronique; Heidenreich, Anna; Thieken, Annegret
    Research on climate change and impacts of natural hazards, such as heat waves, on human health has increased in recent years. Various approaches are used to study people’s attitudes and actions in this context, but little is known about the extent to which different modes or other environmental variables influence the results. Therefore, we ex- amined differences between surveys in three German cities, compared survey modes and investigated the influence of the temperature on the day of the survey and the previous days. We conducted two surveys on the topics of climate change risk perception and heat risk perception. In summer and autumn of 2019, in total 1,417 people from the three medium-sized German cities of Potsdam, Remscheid and Würzburg were surveyed via telephone or online. In sum- mer of 2020, 280 people were surveyed face-to-face in public parks in Potsdam. Climate change risk perception, the perception of heat waves as a health threat and the knowledge of heat warnings differed depending on place of resi- dence, survey mode and temperature. Participants of the online survey showed higher scores of risk perception than participants of the telephone and face-to-face surveys, indicating a self-selection bias. Increased temperature was associated with slightly higher levels of respondents’ heat wave risk perception and, among participants surveyed outside, climate change risk perception. The finding that both survey mode and environmental factors can influence survey results should be heeded when planning or interpreting and comparing studies.
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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.