iTunes Music Analysis Part 2

In response to my post, Music in the Year 2009, AE Subject Matter Expert, Jason Pratt, sent me the following link:

Free Download of Super Analyzer 2.1 from SourceForge

I downloaded the ZIP file, unzipped it, and double-clicked on the extracted JAR file. I pointed the tool at my iTunes database:

C:\Documents and Settings\Scott\My Documents\My Music\iTunes\iTunes Music Library.xml

It provided some interesting data. For example, I most often listen to music at 11:00 AM:

I have mostly been listening to songs from 2009. The blue line is the number of songs I have. The green line is the number of times I have listened to them.

I like rock:

Back in the days of coding in a computer language called C, we had a tool called lint. Lint analyzed your code and warned you of anything that was suspicious. In much the same spirit, SuperAnalyzer conducts a static analysis of your iTunes database and identifies any missing song attributes:

I don't rate my songs. IMHO the point of having something like iTunes is to have your entire music collection on hand rather than play your same top 40 songs. Although I do play some songs more than others, I never rate them all, and then ask the system to play my favorites. So other than the missing ratings, I should not have any missing data. Based on this tool's lint-like feedback, I was able to go into iTunes and edit the info for offending songs and make my data clean.

Making even recreational tasks squeaky clean is alive in the lab.