update for thesis intro

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+\section{Hyperleap and their methods}
+Hyperleap\footnote{\url{http://hyperleap.nl}} is a small company founded in the
+early years of the internet and is located in Nijmegen. Hyperleap is a company
+that presents \textit{infotainment}. \textit{Infotainment} is a concatenation
+of the words \textit{entertainment} and \textit{information} and is a
+specialized form of information, namely about the entertainment industry.
+Hyperleap manages the largest database containing \textit{infotainment}
+containing over $10.000$ events per week on average. It also manages a database
+containing over $54.000$ venues delivering the entertainment. Next to the
+factual information, Hyperleap also provides reviews, previews, background
+information and more via several popular websites specialized on genres or
+categories.
+
+Hyperleap stands compared to other \textit{infotainment} providers because of
+the quality and completeness of the data is comparatively high. This is because
+all information is checked and matched to existing information before it enters
+the database. To ensure the quality of the databases all information enters the
+database in roughly two steps.
+
+In the first step the information is extracted from the raw data sources using
+crawlers or via venue channels. Crawlers are specialized applications that are
+programmed to extract information from one single source. Venue channels are
+ specially made XML feeds that contain already very structured information.
+The extracted information is put in the so called \textit{Temporum}, the
+\textit{Temporum} is a stopping place for the gathered information before it is
+entered in the real database.
+
+The second step in the path of the information is the matching of the data.
+This step is the actual quality checking and matching. Using several
+techniques, employees have to match the incoming information to existing events
+or create new events. This is also a safety net for malfunctioning crawlers,
+when a crawler provides wrong information in the \textit{Temporum} the
+programmer of the crawler has to be informed then. A large amount of the time
+the programmers are busy with repairing crawlers because it is a specialized
+task only doable by people with a computer science background. Because of this
+it is expensive to repair the crawlers.
+
+\section{Goal \& Research question}
+The goal of the project is to relieve the programmer of repairing crawlers all
+the time and make the task of adapting, editing and removing crawlers doable
+for someone without programming experience. In practice this means building an
+application that lets the user create, edit or remove crawlers. For this
+project we focus on RSS\footnote{\url{http://rssboard.org/rss-specification}}
+and Atom\footnote{\url{http://tools.ietf.org/html/rfc5023}} publishing feeds
+only. The program will maintain crawlers that are able to isolate categories of
+information so that the information will appear structured in the
+\textit{Temporum} and make the task of matching the data and entering it less
+expensive.
+
+The program is built so that a programmer can easily add fields or categories
+to the data to make it flexible for changes. 
+
+\section{Why RSS/Atom}
+Information from venues comes in various different format with for each format
+several positive and negative points. For this project we chose to focus on
+RSS/Atom feeds because they are in general already structured and consistent in
+their structure. For example websites change a lot in structure and layout and
+thus making it hard to keep crawlers up to date. RSS/Atom feeds generally only
+change structure because the website or content management system gets migrated
+or upgraded.
+
+RSS/Atom feeds in comparison to websites doesn't have a structural dimension in
+the data. Because of this we have to use different techniques of isolation of
+information than present techniques used for extracting information from
+websites. RSS/Atom feeds are basically two fields with plain text, however the
+text almost always has the same structure and keywords and therefore the
+information can be extracted learning from keywords and structure.
+
+\section{Scientific relevance}
+Currently the techniques for conversion from non structured data to structured
+data are static and mainly only usable by computer science experts. There is a
+great need of data mining in non structured data because the data within
+companies and on the internet is piling up and are usually left to catch dust.
+
+The project is a continuation of the past project done by Roelofs et
+al.\cite{Roelofs2009}. The techniques described by Roelofs et al. are more
+focussed on extracting data from websites and/or already isolated data so it
+can be an addition to the current project.

diff --git a/thesis2/thesis.tex b/thesis2/thesis.tex
index b550ff7..b9349c0 100644 (file)
--- a/thesis2/thesis.tex
+++ b/thesis2/thesis.tex
@@ -1,7 +1,6 @@
-\documentclass[hidelink]{scrbook}
+\documentclass{scrbook}
 
 \usepackage{scrhack}
-\usepackage{lipsum}    % Dummy text
 \usepackage{graphicx}  % Images
 \usepackage{float}     % Better placement float figures
 \usepackage{listings}  % Source code formatting
@@ -66,8 +65,7 @@ data to structured data}
 \clearpage
 
 \chapter{Introduction}
-       \section{Hyperleap}
-       \section{Problem description}
+\input{1.introduction.tex}
 
 \chapter{Methods}
        \section{Application overview}