Idea for speeding up the collapse algorithm when it requires a large number of iterations

[ablack3]

@mvankessel-EMC, @schuemie,

The number of "collapse" iterations you need to do scales with the square of the number of event cohorts, O(n^2) maybe?, so with a large number of event cohorts the number of iterations can be quite large.
Stopping early is one idea to speed up execution but involves information loss (some collapse steps will not be done).

But this collapse step can be partitiond by person. So if a person's data has not changed from iteration 3 to iteration 4 for example we do not need to include that person in interation 5.

So the idea is this: We just remove patients from the cohort table while doing the collapse algorithm if the number of records they have in the table did not change in the previous iteration. This would mean that for later iterations of the algorithm would run much faster than earlier iterations because it would run on fewer records.

Just an idea...