Search for dissertations about: "data management"
Showing result 1 - 5 of 2857 swedish dissertations containing the words data management.
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1. Data management and Data Pipelines: An empirical investigation in the embedded systems domain
Abstract : Context: Companies are increasingly collecting data from all possible sources to extract insights that help in data-driven decision-making. Increased data volume, variety, and velocity and the impact of poor quality data on the development of data products are leading companies to look for an improved data management approach that can accelerate the development of high-quality data products. READ MORE
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2. Corrective maintenance maturity model : problem management
Abstract : Maintenance has become one of the most complex, crucial and costly disciplines within software engineering. Despite this, very few maintenance process models have been suggested. The extant models are too general, covering all maintenance categories, i.e. READ MORE
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3. Traffic Management in Software-Defined Data Center Networks
Abstract : Traffic management in data centers is paramount to improving network and application performance, thereby improving the quality of service by reducing network congestion, packet loss, and latency. However, the deployment and configuration of traffic management techniques are challenging due to diverse data-center traffic characteristics, large data center topologies, and the interplay of different protocols at the routing, transport, and link layer. READ MORE
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4. Information Needs for Water Resource and Risk Management : Hydro-Meteorological Data Value and Non-Traditional Information
Abstract : Data availability is extremely important for water management. Without data it would not be possible to know how much water is available or how often extreme events are likely to occur. The usually available hydro-meteorological data often have a limited representativeness and are affected by errors and uncertainties. READ MORE
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5. Towards Next-Gen Machine Learning Asset Management Tools
Abstract : Context: The proficiency of machine learning (ML) systems in solving many real-world problems effectively has enabled a paradigm shift toward ML-enabled systems. In ML-enabled software, significant software code artifacts (i.e., assets) are replaced by ML-related assets, introducing multiple system development and production challenges. READ MORE