Please use this identifier to cite or link to this item: http://hdl.handle.net/1946/39410
Gathering data has always been a mandatory part of research since the fundamental role of every research project rests in data. Therefore, it is vital to quickly overview the data to grasp the gathered data’s essence. The Sleep Revolution project will contain more than 10,000 sleep measurements. The purpose of this thesis is to find a method to represent better the collected data, which brings forward the questions: i) How can we design and develop a system to extract data from scored sleep measurements? ii) How can layered architecture be used to read, display and filter the extracted data? A layered architecture is used in this thesis, where a system for extracting information from data is designed and developed. The layered architecture in the designed system makes it: i) more reusable, ii) less fragile for changes, and iii) more adaptable for extensions. The industry has often used extraction and filtering to represent and understand data better. However, prior work from the industry that uses filtering and extraction for data has its work hidden, making it difficult to evaluate for research purposes. In research, attempts either lack the variety or volume of data, or the projects do not develop a software architecture to represent their system adequately. For this thesis, the following tests were conducted: i) Scalability tests for reading and filtering, ii) usability tests, iii) AttrakDiff and System Usability Scale Questionnaires and, iv) semi-structured interviews.
The work for this thesis shows a promising start through the design and development of a layered architecture for displaying and filtering the extracted data collected for research purposes. However, the system needs further work and more extensive testing to understand the value and usability of the designed system in more depth. The main contribution of this thesis is the designed layered architecture, the extraction and display system, and a set of design principles to guide others working on a project with similar traits.
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