I have a dataset which contains information about when do people enter and leave a premise. I have the following information in the dataset :
- Person Id
- Time of Entry
- Time of Leaving
The dataset has around 50 unique persons. Each person will have multiple entries corresponding to multiple visits. The data spans over a year so I have quite a lot of entries (around 1 million).
These people can be classified on the basis of the department they work under (2 departments - mutually exclusive) or on basis of their role (4 possible roles - all mutually exclusive)
I was wondering what kind of data analysis can be done with this kind of dataset. I am not looking for straight-forward things like "who spent the most time in building". However things like finding correlation between visits of 2 people would be interesting. So if person A visits the premise, what is the probability that person B would also visit. Since I have only around 50 unique visitors, I think such an analysis is feasible.
Another line of thought was to apply some interval-pattern mining techniques but I am not much familiar with them.
Can someone give me some pointers/ideas about what kind of data products can be build using this or what kind of techniques can be used with such data.
Edit : As discussed in comments, I call it a product in the sense that I do not want some simple or trivial analysis. And I am not looking for any commercially viable idea - just some cool fun idea :)