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Shopping Map
Sherstneva, Sofya ; Kofroň, Jan (advisor) ; Töpfer, Michal (referee)
The goal of this work was to design and implement a web application that would enable creation of shopping maps for better orientation in a store. In addition to this functionality, the application also offers the possibility of establishing friendships between users to work with shared shopping lists. The solution besides of web application contains a mobile application that works in offline mode.
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Machine-learning-based self-adaptation of component ensembles
Töpfer, Michal ; Bureš, Tomáš (advisor) ; Parízek, Pavel (referee)
In the area of distributed self-adaptive smart systems (such as applications of Internet of Things and Cyber-Physical Systems), machine learning has been successfully used in several applications including the prediction of metrics regarding the components in the system (e.g., battery consumption), and pruning of the space of possible adaptations. It is clear that machine learning can be a useful tool in self-adaptive systems. Most of the research works focus on using the machine learning algorithms for a specific task, yet they are (at least partially) lacking in providing a systematic approach to the introduction of machine learning into the architecture of the system. In this thesis, we propose ML-DEECo - a machine-learning-enabled component model for adaptive component architectures. It is based on the concepts of autonomous com- ponents and their ensembles (coalitions) from the DEECo component model. We enrich DEECo with abstractions for specifying machine-learning-based estimates directly in the architecture of the system. The architect can thus focus on the business logic of the application while all the tasks necessary to provide the estimates (such as collecting the data and training the model) are provided by our runtime framework. We provide an implementation of the ML-DEECo runtime in Python and...
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Components for visualization of correlations for IVIS framework
Töpfer, Michal ; Bureš, Tomáš (advisor) ; Kofroň, Jan (referee)
As the number of IoT devices connected to the internet grows, the amounts of data which need to be analysed and visualized also increase. One of the frameworks for creating complex configurable visualizations is IVIS, a web-based open-source framework developed at D3S, MFF UK. In this thesis, we develop and implement components for scatter plot, bubble plot, heatmap chart and histogram chart, which did not exist previously in the framework. These components can be used to visualize correlations among data and to display prop- erties of data distribution. Special emphasis is given to interactivity and configurability of components and a detailed description of the configuration options is provided. We also create a set of examples to show how to use the newly added components together with existing parts of the framework. Existing charts in the framework are also enhanced with the newly introduced concepts. 1
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