🚀 go-pugleaf

I am looking for references that might help me with the following
problem.  (In short, I need references to data smoothing techniques.)

For the UNIX kernel, I can measure the effects of two input
distributions, but I cannot capture them directly.  To simplify, let's
say that the effect is the number of times each routine in the kernel
(total around 5000) is called.

I can linearize this data by numbering routines in the order they're
found in the source.  Related routines will be close to each other (to
a *very* rough approximation -- but probably good enough for now).
I can use the routine number as the X axis, entry count as the Y axis.
This will be a wild point blur:  calls to adjacent routines may differ by
a factor of more than a million.

I need to compare two point blurs to see (very roughly) whether
they're similar or quite different.  What I am thinking of doing is
smoothing them to a curve and matching the curves by eye.  I know
about Tukey/EDA style smoothing.  I know a little bit about LOWESS.  I
don't know enough to say whether or when each is appropriate.  (That
is, I need to read papers or books that say more than just "this is a
technique"; I also need "... and here's where it's most useful, and
here are the dangers".)  Where can I learn what I need?

Also, I need programs to support whatever techniques I use.  I have
ready access to SYSTAT and STATA.

A completely different approach would be fine, too.  I chose this
because it's visual and intuitive.

Thanks.

Brian Marick, marick@cs.uiuc.edu, uiucdcs!marick
Testing Foundations:  Consulting, Training, Tools.