Index of /code/gmane
Name Last modified Size Description
Parent Directory -
d3.layout.cloud.js 2015-12-04 14:05 11K
d3.v2.js 2015-12-04 14:05 248K
gbasic.py 2016-02-15 19:45 945
gline.htm 2015-12-04 14:05 763
gline.jsonp 2015-12-04 14:05 3.7K
gline.py 2016-02-15 19:45 1.6K
gmane.py 2016-02-15 19:45 4.4K
gmodel.py 2016-02-08 18:45 7.7K
gword.htm 2015-12-04 14:05 1.1K
gword.jsonp 2015-12-04 14:05 2.8K
gword.py 2016-02-15 19:45 1.3K
gyear.py 2016-02-15 19:45 1.6K
mapping.sqlite 2016-02-15 14:56 12K
Analyzing an EMAIL Archive vizualizing the data using the
Here is a copy of the Sakai Developer Mailing list from 2006-2014.
You should install the SQLite browser to view and modify the databases from:
The base URL is hard-coded in the gmane.py. Make sure to delete the
content.sqlite file if you switch the base url. The gmane.py file
operates as a spider in that it runs slowly and retrieves one mail
message per second so as to avoid getting throttled. It stores all of
its data in a database and can be interrupted and re-started
as often as needed. It may take many hours to pull all the data
down. So you may need to restart several times.
To give you a head-start, I have put up 600MB of pre-spidered Sakai
If you download and unzip this, you can "catch up with the
latest" by running gmane.py.
Navigate to the folder where you extracted the gmane.zip
Here is a run of gmane.py getting the last five messages of the
sakai developer list:
Mac: python gmane.py
How many messages:10
firstname.lastname@example.org 2005-12-09T13:32:29+00:00 re: lms/vle rants/comments
email@example.com 2005-12-09T13:32:31-06:00 re: sakaiportallogin and presense
firstname.lastname@example.org 2005-12-09T13:42:24+00:00 re: lms/vle rants/comments
The program scans content.sqlite from 1 up to the first message number not
already spidered and starts spidering at that message. It continues spidering
until it has spidered the desired number of messages or it reaches a page
that does not appear to be a properly formatted message.
Sometimes there is missing a message. Perhaps administrators can delete messages
or perhaps they get lost - I don't know. If your spider stops, and it seems it has hit
a missing message, go into the SQLite Manager and add a row with the missing id - leave
all the other fields blank - and then restart gmane.py. This will unstick the
spidering process and allow it to continue. These empty messages will be ignored in the next
phase of the process.
One nice thing is that once you have spidered all of the messages and have them in
content.sqlite, you can run gmane.py again to get new messages as they get sent to the
list. gmane.py will quickly scan to the end of the already-spidered pages and check
if there are new messages and then quickly retrieve those messages and add them
The content.sqlite data is pretty raw, with an innefficient data model, and not compressed.
This is intentional as it allows you to look at content.sqlite to debug the process.
It would be a bad idea to run any queries against this database as they would be
The second process is running the program gmodel.py. gmodel.py reads the rough/raw
data from content.sqlite and produces a cleaned-up and well-modeled version of the
data in the file index.sqlite. The file index.sqlite will be much smaller (often 10X
smaller) than content.sqlite because it also compresses the header and body text.
Each time gmodel.py runs - it completely wipes out and re-builds index.sqlite, allowing
you to adjust its parameters and edit the mapping tables in content.sqlite to tweak the
data cleaning process.
Running gmodel.py works as follows:
Mac: python gmodel.py
Loaded allsenders 1588 and mapping 28 dns mapping 1
1 2005-12-08T23:34:30-06:00 email@example.com
251 2005-12-22T10:03:20-08:00 firstname.lastname@example.org
501 2006-01-12T11:17:34-05:00 email@example.com
751 2006-01-24T11:13:28-08:00 firstname.lastname@example.org
The gmodel.py program does a number of data cleaing steps
Domain names are truncated to two levels for .com, .org, .edu, and .net
other domain names are truncated to three levels. So si.umich.edu becomes
umich.edu and caret.cam.ac.uk becomes cam.ac.uk. Also mail addresses are
forced to lower case and some of the @gmane.org address like the following
are converted to the real address whenever there is a matching real email
address elsewhere in the message corpus.
If you look in the content.sqlite database there are two tables that allow
you to map both domain names and individual email addresses that change over
the lifetime of the email list. For example, Steve Githens used the following
email addresses over the life of the Sakai developer list:
We can add two entries to the Mapping table
email@example.com -> firstname.lastname@example.org
email@example.com -> firstname.lastname@example.org
And so all the mail messages will be collected under one sender even if
they used several email addresses over the lifetime of the mailing list.
You can also make similar entries in the DNSMapping table if there are multiple
DNS names you want mapped to a single DNS. In the Sakai data I add the following
iupui.edu -> indiana.edu
So all the folks from the various Indiana University campuses are tracked together
You can re-run the gmodel.py over and over as you look at the data, and add mappings
to make the data cleaner and cleaner. When you are done, you will have a nicely
indexed version of the email in index.sqlite. This is the file to use to do data
analysis. With this file, data analysis will be really quick.
The first, simplest data analysis is to do a "who does the most" and "which
organzation does the most"? This is done using gbasic.py:
Mac: python gbasic.py
How many to dump? 5
Loaded messages= 51330 subjects= 25033 senders= 1584
Top 5 Email list participants
Top 5 Email list organizations
You can look at the data in index.sqlite and if you find a problem, you
can update the Mapping table and DNSMapping table in content.sqlite and
There is a simple vizualization of the word frequence in the subject lines
in the file gword.py:
Mac: python gword.py
Range of counts: 33229 129
Output written to gword.js
This produces the file gword.js which you can visualize using the file
A second visualization is in gline.py. It visualizes email participation by
organizations over time.
Mac: python gline.py
Loaded messages= 51330 subjects= 25033 senders= 1584
Top 10 Oranizations
['gmail.com', 'umich.edu', 'uct.ac.za', 'indiana.edu', 'unicon.net', 'tfd.co.uk', 'berkeley.edu', 'longsight.com', 'stanford.edu', 'ox.ac.uk']
Output written to gline.js
Its output is written to gline.js which is visualized using gline.htm.
Some URLs for visualization ideas:
As always - comments welcome.
-- Dr. Chuck
Sun Sep 29 00:11:01 EDT 2013