Monday 14 May 2018

Filtering outliers from Oracle APEX activity logs

Last year I described a simple test case that described how to remove outliers from a fictional dataset using the STDDEV() analytical function .

I want to follow this up with a practical case using one of my favourite data sets - the apex_workspace_activity_logs that record who opened what page, in what context, and how long it took to generate.

I've been keeping an eye on the performance of a particular page, after making a few performance adjustments to some conditions. Unfortunately, we had an unrelated anomaly that pushed average pages times quite high for a short period. Needless to say, this set of outliers transformed my beautiful performance indicating lines to a boxy bell curve.

Oracle APEX page performance data with extreme outlier

A great feature with Oracle JET is the ability to hide certain series, on click within the legend.
In this case I just wanted to ignore the MAX line for this post, which in this chart forms the secondary y-axis.

OracleJET Region Attributes - rescale
Our clients really enjoy this particular feature (so do I), so thanks to the JET team for building such a device, and the APEX team for integrating it.

This graph shows results where I modified the query to filter the outliers, on demand.

Performance graph with outlier removed

Looks like the adjustments to the conditions worked! The trend is downwards.

I tried a few variations to control the switch, but this seemed to perform the most predictably, although I'm not happy with the hardcoded number.

select [aggregate stuff]
from (
 select [all columns]
,case when :P23_IGNORE_OUTLIERS = 'Y' then
  -- only bother calculating when filtering them out
  stddev(elapsed_time) over (order by null)
end as the_stddev
from [activity logs]
where [time/page is desired]
-- only when elapsed time less than 2 standard deviations gets 95% of your data
where (elapsed_time < 2*the_stddev )
I have a generic example of this on I pay attention to these aggregates for our performance reports
  • Median - what most users are experiencing
  • Average - a typical user experience, influenced by extremes
  • Moving average - general trend of visits, spread over a few days. An attempt to normalise local events
  • Max - what's the worst some people are experiencing?
Here are some other activity log queries you may find interesting.

Happy graphing!


Juergen Schuster said...

Great Post, very helpful and I would never have the nerves to find this out by myself :-)

Scott Wesley said...

Thanks Juergen. Eliminating outliers in this data is something that's been bugging me for a while, and I've played with stddev once or twice before.