From: Mart Lubbers <mart@martlubbers.net>
Date: Tue, 9 Dec 2014 13:29:20 +0000 (+0100)
Subject: stuff added in methods
X-Git-Url: https://git.martlubbers.net/?a=commitdiff_plain;h=1f6d0e9b8152b86c1b008be849c9abadafd50679;p=bsc-thesis1415.git

stuff added in methods
---

diff --git a/thesis2/3.methods.tex b/thesis2/3.methods.tex
index d8c0e3a..8670cd0 100644
--- a/thesis2/3.methods.tex
+++ b/thesis2/3.methods.tex
@@ -1,11 +1,45 @@
-\section{Application overview and workflow}
-The program can be divided into two main components namely the \textit{Crawler
-application} and the \textit{Input application}. The components are strictly
-separated by task and by application. The crawler is an application dedicated
-to the sole task of periodically crawling the sources asynchronously. The input
-is a web interface to a set of tools that can create, edit, remove and test
-crawlers via simple point and click user interfaces that can be worked with by
-someone without a computer science background.
+\section{Internals of the crawler generation module}
+Data marked by the user is in principle just raw html data that contains a
+table with for every RSS feed entry a table row. Within the html the code
+severel markers are placed to make the parsing more easy and removing the need
+of an advanced HTML parser. When the user presses submit a http POST request is
+prepared to send all the gathered data to the backend to get processed. The
+sending does not happen asynchronously, so the user has to wait for the data to
+be processed. Because of the sychronous data transmission the user is notified
+immediatly when the crawler is succesfully added. The data preparation that
+makes the backend able to process it is all done on the user side using
+Javascript. 
+
+When the backend receives the http POST data it saves all the raw data and the
+data from the information fields. The raw data is processed line by line to
+extract the entries that contain user markings. The entries containing user
+markings are stripped from all html while creating an data structure that
+contains the locations of the markings and the original text. All data is
+stored, for example also the entries without user data, but not all data is
+processed. The storage of, at first sight, useless data is done because later
+when the crawlers needs editing the old data can be used to update the crawler.
+
+When the entries are isolated and processed they are converted to node-lists. A
+node-list is a literal list of words where a word is interpreted in the
+broadest sense, meaning that a word can be a character, a single byte, a string
+or basically anything. These node-lists are in this case the original entry
+string where all the user markers are replaced by single nodes. As an example
+lets take the following entry and its corresponding node-list assuming that the
+user marked the time, title and date correctly. Markers are visualized by
+enclosing the name of the marker in angle brackets.
+\begin{flushleft}
+	Entry: \texttt{19:00, 2014-11-12 - Foobar}\\
+	Node-list: \texttt{['<time>', ',', ' ', '<date>', ' ', '-', ' ', '<title>']}
+\end{flushleft}
+
+The last step is when the entries with markers are then processed to build
+node-lists. Node-lists are basically lists of words that, when concatenated,
+form the original entry. A word isn't a word in the linguistic sense. A word
+can be one letter or a category. The node-list is generated by putting all the
+separate characters one by one in the list and when a user marking is
+encountered, this marking is translated to the category code and that code is
+then added as a word. The nodelists are then sent to the actual algorithm to be
+converted to a graph representation.
 
 \section{Minimizing DAWGs}
 The first algorithm to generate DAG's was proposed by Hopcroft et
@@ -51,80 +85,41 @@ For example constructing a graph that from the entry: \textit{a,bc} and
 	}
 \end{figure}
 
-\section{Input application}
-\subsection{Components}
-Add new crawler
-
-Editing or remove crawlers
-
-Test crawler
-
-Generate xml
-
-
-\section{Crawler application}
-\subsection{Interface}
-
-\subsection{Preprocessing}
-When the data is received by the crawler the data is embedded as POST data in a
-HTTP request. The POST data consists of several fields with information about
-the feed and a container that has the table with the user markers embedded.
-After that the entries are extracted and processed line by line.
-
-The line processing converts the raw string of html data from a table row to a
-string. The string is stripped of all the html tags and is accompanied by a
-list of marker items. The entries that don't contain any markers are left out
-in the next step of processing. All data, including entries without user
-markers, is stored in the object too for possible later reference, for example
-for editing the patterns.
-
-The last step is when the entries with markers are then processed to build
-node-lists. Node-lists are basically lists of words that, when concatenated,
-form the original entry. A word isn't a word in the linguistic sense. A word
-can be one letter or a category. The node-list is generated by putting all the
-separate characters one by one in the list and when a user marking is
-encountered, this marking is translated to the category code and that code is
-then added as a word. The nodelists are then sent to the actual algorithm to be
-converted to a graph representation.
-
-\subsection{Defining categories}
-pass
-
-\subsection{Process}
-Proposal was written
-
-
-First html/mail/fax/rss, worst case rss
-
-
-After some research and determining the scope of the project we decided only to
-do RSS, this because RSS tends to force structure in the data because RSS feeds
-are often generated by the website and thus reliable and consistent. We found a
-couple of good RSS feeds.
-
-
-At first the general framework was designed and implemented, no method yet.
-
-
-Started with method for recognizing separators.
-
-
-Found research paper about algorithm that can create directed acyclic graphs
-from string, although it was designed to compress word lists it can be
-(mis)used to extract information.
-
-
-Implementation of DAG algorithm found and tied to the program.
-
-
-Command line program ready. Conversation with both supervisors, gui had to be
-made.
-
-Step by step gui created. Web interface as a control center for the crawlers.
-
-
-Gui optimized.
-
-
-Concluded that the program doesn't reach wide audience due to lack of well
-structured rss feeds.
+%\subsection{Process}
+%Proposal was written
+%
+%
+%First html/mail/fax/rss, worst case rss
+%
+%
+%After some research and determining the scope of the project we decided only to
+%do RSS, this because RSS tends to force structure in the data because RSS feeds
+%are often generated by the website and thus reliable and consistent. We found a
+%couple of good RSS feeds.
+%
+%
+%At first the general framework was designed and implemented, no method yet.
+%
+%
+%Started with method for recognizing separators.
+%
+%
+%Found research paper about algorithm that can create directed acyclic graphs
+%from string, although it was designed to compress word lists it can be
+%(mis)used to extract information.
+%
+%
+%Implementation of DAG algorithm found and tied to the program.
+%
+%
+%Command line program ready. Conversation with both supervisors, gui had to be
+%made.
+%
+%Step by step gui created. Web interface as a control center for the crawlers.
+%
+%
+%Gui optimized.
+%
+%
+%Concluded that the program doesn't reach wide audience due to lack of well
+%structured rss feeds.