So, my latest idea for a solution is to write a python script that will import the section (last X days) of data from the EventLogging tables that we're interested in into a temporary sqlite database, then proceed with removing the upper and lower percentiles of the data, according to any column grouping that might be necessary. And finally, once the data preprocessing is done in sqlite, run similar queries as before to export the mean, standard deviation, etc. for given metrics to tsvs. I think using sqlite is cleaner than doing the preprocessing on db1047 anyway.

It's quite an undertaking, it basically means rewriting all our current SQL => TSV conversion. The ability to use more steps in the conversion means that we'd be able to have simpler, more readable SQL queries. It would also be a good opportunity to clean up the giant performance query with a bazillion JOINS: https://gitorious.org/analytics/multimedia/source/a949b1c8723c4c41700cedf6e9e48c3866e8b2f4:perf/template.sql which can actually be divided into several data sources all used in the same graph.

Does that sound like a good idea, or is there a simpler solution out there that someone can think of?

Well, I think this sounds like we need to seriously evaluate how people are using EventLogging data and provide this sort of analysis as a feature.  We'd have to hear from more people but I bet it's the right thing to do long term.