[cchianel]

I written a data structure for grouping intervals into disjoint connected clusters. Each cluster in the data structure is a connected component of the interval space (see https://en.wikipedia.org/wiki/Connected_space for the definition of connected component).

This is useful to determining maximum concurrent use of a resource; for instance, if there are only 3 projectors, a maximum of 3 lessons that require projectors can overlap at any given time.

The basic idea is to use a sorted multi-set of each interval's end points, doing case analysis on insertion to determine what interval clusters to merge; removal re-computes the entire cluster the removed range was in, splitting the cluster into two or more when gaps are encountered.

For the implementation, see https://github.com/TimefoldAI/timefold-solver/blob/main/core...

As a side effect of keeping track of connected clusters, the data structure also keeps track of gaps between clusters (which could be used to find the next availability).

For how it is used in practice, there an example in Timefold's docs: https://docs.timefold.ai/timefold-solver/latest/constraints-...

A simpler algorithm can be used when each interval represent a time grain (fixed length, no two intervals overlap). See https://github.com/TimefoldAI/timefold-solver/tree/main/core... for that.

[grovesNL]

Thank you for the reference, I’ll take a look! I like the idea of being able to use connected components somehow if it could avoid some of the overhead of range queries during updates. I’ve been considering whether there might be a way to apply union-find in a way that would still be faster than some kind of bitset representation.