Statistics Management for Partitioned Objects in PeopleSoft

I have implemented partitioned objects in a number of PeopleSoft systems on Oracle. Recently, I was working on a system where a table was partitioned into weekly range partitions, and I encountered a performance problem when Oracle's automatic maintenance window job to collect statistics did not run between populating the new partition for the first time, and running a batch process that referenced that partition. Oracle, understandably produced a execution plan for a statement that assumed the partition was empty, but as the partition actually had quite a lot of data, the statement ran for a long time. The solution was to tell Oracle the truth by gathering statistics for that partition. However, I didn't want to refresh the statistics for the whole table. There were many partitions with historical data that has not changed, so I don't need to refresh those partitions. I only need to refresh just the stale partitions, and here is the problem. Unfortunately, dbms_stats package will let you gather stale and missing statistics for all tables in a given schema, or the whole database, but not for a named table. It is not completely unreasonable, if you are targeting a single table then you ought to know what needs to be refreshed. I have written a PL/SQL procedure to flush the table monitoring statistics to the data dictionary and determine whether the statistics on the table, any of its partitions and sub-partitions are stale or missing, and if so gather statistics on those segments. It uses (I believe) the same criteria as dbms_stats to determine stale objects: 10% change relative to last gathered statistics, or if the segment has been truncated. I have incorporated the new refresh_stats procedure into my PL/SQL packaged procedure wrapper which can be called by the %UpdateStats PeopleCode macro via a customised DDL model. The new procedure is only called for partitioned tables. All that is necessary it to use the %UpdateStats macro in an Application Engine program. This is all still work-in-progress, but so far, the results are encouraging.