One of our goals was to understand how different measurement points in the water network are correlated.
This knowledge can be used to understand inter-effects at different places of the water network in terms of water quality.
Also, knowing how much different measurements sites are correlated can help to solve sensor problems and issues.
We have exported the water data to python pandas dataframes, then we have run the cross correlations and distance computations in python.
Also, we have created a geo-map visualization of the different sensors using Kibana on Elasticsearch. we have converted data from OSI PI to Elasticsearch using python code we have written.
This is a link to the Kibana Dashboard:
We didn't have time to finish all what we have planned. But in any case, some screenshots from the results we did get are in the slides:
The code is in github: