Product Vision Statement » Historie » Verze 5
Roman Kalivoda, 2021-03-30 11:15
Extend Project goals with description of sys components and used technologies
| 1 | 1 | Roman Kalivoda | h1. Product Vision Statement (WIP) |
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| 3 | h2. Project Goals |
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| 5 | 5 | Roman Kalivoda | Creating an application that will based on weather input, predict the attendance in class. User will be able to input their own weather information or choose a prediction based on current weather information or prediction for future days. |
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| 7 | h3. System parts & technologies |
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| 9 | h4. Server (backend) part |
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| 11 | There will be a server application which: |
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| 12 | * will retrain prediction model when new data are available (or a new model is defined by admin), |
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| 13 | * will run predictions based on client app requests and send the response once it is ready. |
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| 15 | We decided that the backend will be developed in C# and .NET platform. |
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| 17 | h4. Web frontend app |
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| 19 | There will be a WebGL application: |
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| 20 | * user will be able to specify arbitrary weather conditions (e.g. temperature, precipitation) or use an automatic weather forecast, |
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| 21 | * user will be able to specify an arbitrary classroom at UWB, |
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| 22 | * these input data will be made into a web request and sent to the server, |
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| 23 | * The prediction result will be shown to the user when the response is received. |
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| 25 | The app will be written in C# and Unity framework. |
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| 27 | h4. Android app |
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| 29 | There might be an android app with functionality similar to the web frontend. The app will also be developed with C# and Unity. |
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| 30 | 1 | Roman Kalivoda | |
| 31 | 3 | Eliška Mourycová | h3. Happy Day use-case |
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| 33 | The user will specify a date and classroom (e.g. UC-336) for which they wish to get the prediction of attendance. It will be possible to choose to have the weather forecast data for the given day downloaded automatically or input manually. The output of the app will be a text field saying how high the attendance the model predicts (e.g. very high). |
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| 35 | 1 | Roman Kalivoda | h2. Project Plan |
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| 38 | h2. Stakeholders |
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| 40 | * Development Team |
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| 41 | * Project Sponsor |
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| 42 | * Project Mentor |
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| 43 | * Users: lecturers |
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| 44 | * Users: students |
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| 46 | h2. Risks |
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| 48 | h3. Available data are too crude |
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| 49 | 4 | Roman Kalivoda | |
| 50 | Chances are that the data are not specific enough to make proper predictions for single classrooms. Hopefully, the model could be improved gradually when there is more data available. In the meantime, we could modify the app to show just the prediction for a whole building. |
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| 52 | h3. Our effort estimation is grossly underestimated |
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| 54 | We should define/negotiate a minimum viable product and prioritize individual features. |
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| 56 | h3. We proposed an unsuitable technological stack |
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| 58 | _(TBD)_ |