Search for dissertations about: "experimental data"
Showing result 1 - 5 of 3382 swedish dissertations containing the words experimental data.
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1. Dynamic Adaptations of Synchronization Granularity in Concurrent Data Structures
Abstract : The multicore revolution means that programmers have many cores at their disposal in everything from phones to large server systems. Concurrent data structures are needed to make good use of all the cores. Designing a concurrent data structure that performs well across many different scenarios is a difficult task. READ MORE
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2. Experimental Governance : Capacity and legitimacy in local governments
Abstract : Contemporary planning and governance of cities involves practices of experiments and trials in urban experiments, collaborative platforms, and urban development projects with high ambitions for sustainability and innovative solutions. These practices of experimental governance can be seen as new policy instruments that include actors from all sectors of society in collective problem-solving. READ MORE
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3. Parallel Data Streaming Analytics in the Context of Internet of Things
Abstract : We are living in an increasingly connected world, where the ubiquitously sensing technologies enable inter-connection of physical objects, as part of Internet of Things (IoT), and provide continuous massive amount of data. As this growth soars, benefits and challenges come together, which requires development of right tools in order to extract valuable information from data. READ MORE
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4. Smart Cities and Big Data Analytics : A Data-Driven Decision-Making Perspective
Abstract : The phenomenon of digitalization has led to the emergence of a new term—big data. Big data refers to the vast volumes of digital data characterized by its volume, velocity, variety, veracity, and value. READ MORE
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5. Random Forest for Histogram Data : An application in data-driven prognostic models for heavy-duty trucks
Abstract : Data mining and machine learning algorithms are trained on large datasets to find useful hidden patterns. These patterns can help to gain new insights and make accurate predictions. Usually, the training data is structured in a tabular format, where the rows represent the training instances and the columns represent the features of these instances. READ MORE