Building Graph Tables

Tomaz Kastrun uses a set of e-mails as his SQL Server 2017 graph table data source:

To put the graph database to the test, I took bunch of emails from a particular MVP SQL Server distribution list (content will not be shown and all the names will be anonymized). On my gmail account, I have downloaded some 90MiB of emails in mbox file format. With some python scripting,  only FROM and SUBJECTS were extracted:

writer.writerow(['from','subject'])
for index, message in enumerate(mailbox.mbox(infile)): content = get_content(message) row = [ message['from'].strip('>').split('<')[-1], decode_header(message['subject'])[0][0],"|" ] writer.writerow(row)

This post walks you through loading data, mostly.  But at the end, you can see how easy it is to find who replied to whose e-mails.

Related Posts

Vectors for Programmers

John Mount has a couple of videos available: We have just released two new free video lectures on vectors from a programmer’s point of view. I am experimenting with what ideas do programmers find interesting about vectors, what concepts do they consider safe starting points, and how to condense and present the material. Click through […]

Read More

Sentiment Analysis with Python

Bruno Stecanella shows us how to use MonkeyLearn to perform sentiment analysis in Python: Sentiment analysis is a set of Natural Language Processing (NLP) techniques that takes a text (in more academic circles, a document) written in natural language and extracts the opinions present in the text. In a more practical sense, our objective here is to take a text […]

Read More

Categories

June 2017
MTWTFSS
« May Jul »
 1234
567891011
12131415161718
19202122232425
2627282930