ASWI - Pokročilé softwarové inženýrství » ASWI 2021 » OpenData vlastní téma (KIV) - Tři Mušketýři

h1. Application predicting class attendance based on weather

h2. Data

The application uses datasets containing historical information about the weather on campus and attendance based on JIS card verifications and historical timetable information

* JIS data http://opendata.zcu.cz/Snimace-JIS.html

* Timetable data http://opendata.zcu.cz/Obsazeni-mistnosti.html

* Weather data http://opendata.zcu.cz/Energeticky-dispecink.html

* Authentication system http://opendata.zcu.cz/Autentizacni-system.html

The timetable provides the baseline attendance, JIS card data can be used to find out the real attendance (and therefore fluctuation in attendance). The authentication data could be used to find out how many people have logged in during classes.

h3. Weather

Weather data for model training contains the following information: date, temperature, wind, rain, light in k lux

User can input weather values manually, by selecting options from a form

* values: temperature, wind, rain, sunny/overcast/partly cloudy

There also is an option for data to be automatically downloaded from a server upon request from the user.

Current options for data sources

* RSS from yr.no http://www.kanonbra.com/rss/yr_forecast_rss.php?language=en_US&location=%C4%8Ceahkka/Plze%C5%88sk%C3%BD_Kraj/Plze%C5%88

* RSS from yahoo weather (might be a problem with authorization) https://www.yahoo.com/news/weather/czech-republic/plze%C5%88sk%C3%BD/plze%C5%88-796166/

* JSON from wttr.in http://wttr.in/Plzen,czechia?format=j1

App provides the ability to ask for data from today or days in the future, and give hourly time specifics (this time will be then approximated into values morning, noon, afternoon, night)

Again the app will use the following values: temperature, wind, rain, sunny/overcast/partly cloudy

Notes:

Values sunny/overcast/partly cloudy will be translated into lux values

The temperature will be evaluated with certain tolerance (probably determined experimentally)

h3. Datasets

Datasets are updated every once in a while with new data. How often update?

-> has to trigger re-training the prediction model, probably do not want to update too often.

Detect when there's new data, check every day? every x days?

New OpenData is added monthly. How complex will be the training? Can it be done on the target device? Will there be a server from which a new model could be downloaded?

h3. Prediction

a) Naive Bayes classifier

Could classify days into high-medium-low attendance days, or more granularly (very high, very low...).

b) Neural network

Could provide a percentual likelihood of per-student attendance.

Risks:

We have very little experience with neural networks. Bayesian classification might provide good results with much less effort.

h2. Design

h3. The output of the Algorithm

We need to determine what should be the actual prediction:

* A concrete number of students in a class,

* information about relative occupancy rate.

h2. Motivation

TBD

h2. Implementation

The application is a mobile app, possibly WebGL app

It is developed in unity which provides more possibilities for export with modifications for given platforms

- prediction model (pluses and minuses ?)

	- neuron network

	- bayes