This thesis describes a way to represent domain knowledge as functions. Those functions can be composed and used for better predicting time needed for a task. These functions can aggregate data from different systems to provide a more complete view of the contextual environment without the need to consolidate data into one system. These functions can be crafted to make a more precise time prediction for a specific task that needs to be carried out in a specific context. We describe a possible way to structure and model data that could be used with the functions. As a proof of concept, a prototype was developed to test an envisioned scenario with simulated data. The prototype is compared to predictions using min, max and average values from previous experience. The result shows that domain knowledge, represented as functions can be used for improved prediction. This way of defining functions for domain knowledge can be used as a part of a CBR system to provide decision support in a problem domain where information about context is available. It is scalable in the sense that more context can be added to new tasks over time and more functions can be added and composed. The functions can be validated on old cases to assure consistency.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:mdh-41105 |
| Date | January 2018 |
| Creators | Roslund, Anton |
| Publisher | Mälardalens högskola, Akademin för innovation, design och teknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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