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Optimization of KPI Processing
 Šulc, Ondřej ; Bartík, Vladimír (referee) ; Hynek, Jiří (advisor)
This thesis deals with the optimization of data processing from IoT sensors of smart cities into the form of key performance indicators (abbr. KPI). KPIs are a mean of monitoring a large amount of data and expressing the status of performance factors affecting the prosperity of the entire city. Data processing in this form is a computationally demanding process, but it consists of a large number of mutually independent calculations. Therefore the goal of this thesis was to perform optimization using parallelization. In parallel processing, calculations can be divided between multiple threads, enabling all available computing resources (CPU cores) to be fully used. This concept was practically implemented in the Smart City project of Logimic company. However, the project is built on the Node.js platform, and when using parallelization there are complications with the use of libraries for object-relational mapping (abbr. ORM). ORM libraries on the Node.js platform are not always ready to work in a parallel environment. This problem is solved by creating a separate instance of the used library for each parallel thread. The thesis focuses on reducing the overhead associated with this and also on the correct distribution of work between parallel threads so that all cores are used equally. The results of this work prove that optimizing IoT data processing using parallelization leads to a significant speedup that conforms to Amdahl's law, as overhead problems can be reduced to a negligible minimum.
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