Author: John Liaperdos (ioannis.liaperdos@gmail.com)
Last update: April 28, 2015
Description: Provides an example of a PhD Thesis for Universidad de Antioquia
using the teipel-thesis-en pdfLaTeX class.
Character encoding: UTF-8
use the "modern" or "classic" option to switch between
a modern or classic font, respectively.
add/remove the "hyperref" option to enable/disable hyperlinks:
(remember to remove auxiliary files after adding/removing
the "hyperref" option).
add/remove the "printer" option to typeset a printer-friendly
(grayscale)/color version of the thesis.
use the "watermark" option to indicate a draft copy of the thesis
use the "histinit" option to enable "historiated initials".
(If used, all chapter initials declared by the \InitialCharacter{}
macro are enlarged. If ommitted, arguments of \InitialCharacter{}
are typeset as normal text.)
use the "plain" option to disable tikz graphics in title page
and part/chapter headers (might help to avoid compilation timeouts).
Note that "plain" disables CD label and CD cover creation.
use the "noindex" option to (hopefully) avoid compilation timeouts
when compiling online (disables index generation - note that "\indexGR",
"\index" invocations need not be removed when toggling this option).
use the "frontispiece" option to typeset a frontispiece at the back of the cover page
use the `letter' option for a US letter page layout, instead of A4
\documentclass[modern,hyperref,watermark,histinit,frontispiece,plain]{teipel-thesis-en}
In Email Analytics, our main focus on criminal and civil investigation from large email dataset. It is very difficult to deal with challenging task for investigator due to large size of email dataset. This paper offer an interactive email analytics various to current and manually intensive technique is used for search evidence from large email dataset. In investigation process, many emails are irrelevant to the investigation so it will force investigator to search carefully through email in order to find relevant emails manually. This process is very costly in terms of money and times. To help to investigation process. We combine Elasticsearch, Logstash and Kibana for data storing, data preprocessing, data visualization and data analytics and displaying results. In this process reduce the number of email which are irrelevant for investigation. It shows the relationship between them and also analyzing the email corpus based on topic relation using text mining.