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- Checking datasets
- Analyzing constraints for input values
- Checking for mistakes and units
- Planning Microiterations
- Implement Software
We analyzed the datasets and identified constrained for some of the variables. The background is that it should be avoided to process false data. Using a validation, the process "detects" if, e.g., a sensor would be defect or a dataset has been manipulated and avoids processing such data.
Weather Dataset
- Temperatures are measured in Fahrenheit (
$\texttt{°F}$ ) - Constraints for temperatures:
- Minimum Temperature:
$\underbrace{ -90 \texttt{°C} - 10 \texttt{°C}}_{\texttt{min. temperature + offset}} \approx -148 \texttt{°F}$ - Maximum Temperature:
$\underbrace{57 \texttt{°C} + 10 \texttt{°C}}_{\texttt{max. temperature + offset}} \approx 153 \texttt{°F}$
- Minimum Temperature:
❗ Important
Usually, constraints such as the one for temperatures are discussed and determined with the customer. For the purpose of this challenge, we skip that step and determine the temperatures based on the Wikipedia page for Temperature Extremes and add an offset of$\pm10 \texttt{°C}$ .
ℹ️ Note We checked out the original dataset and fixed a typo in a column name of the weather dataset
MnR
). Furthermore, we identified an empty row which was combined with another column to a single column in the CSV (1Hrp
+TPcpn
)
Football Dataset
- Goals are measured in integers
- The number of goals have to be
$\geq 0$
We seperate the challenge first into two tasks. Then, we split each task into microtasks (i.e., micro iterations).
We divide the challenge in following subtasks:
- Model the data
- Add feature to calculated difference
- Add reader for CSV
- Add feature to identify record with the smallest difference
- Create modell
- Adapt factory to produce
FootballRecord
s - Adapt
Record
interface to id of type String
- Clean up code
- Add wiki pages
- Document design decisions and class diagrams