Search for dissertations about: "DATA SCIENCE"
Showing result 1 - 5 of 5231 swedish dissertations containing the words DATA SCIENCE.
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1. Data Stream Mining and Analysis : Clustering Evolving Data
Abstract : Streaming data is becoming more prevalent in our society every day. With the increasing use of technologies such as the Internet of Things (IoT) and 5G networks, the number of possible data sources steadily increases. Therefore, there is a need to develop algorithms that can handle the massive amount of data we now generate. READ MORE
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2. External Data Incorporation into Data Warehouses
Abstract : Most organizations are exposed to increasing competition and must be able to orient themselves in their environment. Therefore, they need comprehensive systems that are able to present a holistic view of the organization and its business. READ MORE
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3. 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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4. Uncovering biomarkers and molecular heterogeneity of complex diseases : Utilizing the power of Data Science
Abstract : Uncovering causal drivers of complex diseases is yet a difficult challenge. Unlike single-gene disorders complex diseases are heterogeneous and are caused by a combination of genetic, environmental, and lifestyle factors which complicates the identification of patient subgroups and the disease causal drivers. READ MORE
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5. Learning from Complex Medical Data Sources
Abstract : Large, varied, and time-evolving data sources can be observed across many domains and present a unique challenge for classification problems, in which traditional machine learning approaches must be adapted to accommodate for the complex nature of such data. Across most domains, there is also a need for machine learning models that are both well-performing and interpretable, to help provide explanations of a model's decisions that stakeholders can trust and take appropriate actions with. READ MORE