Much like the previous entries, we wanted to take various analytics tools for a spin while also trying to answer quasi-real world queries. We're using Datameer and Karmasphere this time around, and our data source is the Enron corpus. This time we're going to look at queries in two different categories - one in product evaluation and another two in IT. Short and sweet. The questions we want to answer are:
- Who's been communicating with company X the most?
- How many duplicate emails are there?
- How much space do the duplicate emails take up?
If a company starts evaluating one of our products, it only makes sense to have the folks who've been talking to them the most follow up on their eval. The question arises: who is that person? We specifically decided to figure out who in Enron had been talking to TXU (a Texas oil company) the most:
As one can see, it'd probably be best to have Farmer, Tisdale, or Hanks do any followups, supposing that they are in the correct department.
Emails can take up serious hard disk space. A company may not want to lose all record of an email by deletion, but what if they only deleted duplicates? How would they find them? With Karmasphere, one can write a query like this:
SELECT body, timesent, subject, torecipient, bccrecipient, ccrecipient, sender, COUNT(*) FROM enronData GROUP BY body, timesent, subject, torecipient, bccrecipient, ccrecipient, sender HAVING ( COUNT(*) > 1 )
This query basically groups all emails that have the same headers together, and spits them out. We could then count the occurrences for each of these to find out how many duplicates we actually have. Is this worth it though? How much hard disk space do we actually save? Well, assuming one byte per character in each email, we can do something like this query to get an approximation:
SELECT copies, SUM(product) FROM (SELECT body, COUNT(*) as copies, LENGTH(body) as body_length, (copies - 1) * LENGTH(body) as product FROM enronData GROUP BY body, timesent, subject, torecipient, bccrecipient, ccrecipient, sender HAVING ( copies > 1 )) tabulation GROUP BY copies
This spits out a long list of emails and their sizes grouped by their duplication. It would only be a simple matter of summing all of that up to see the actual wasted space. Unfortunately, all of this effectively adds up to a single number, so no charts this go around.
This concludes our segue into analytics for various roles. We had fun taking a spin with the various tools! If there's some aspect of any of this you have a question about, or if there's an angle of analysis you'd like to see, drop us a line in the comments below!