thesis update

diff --git a/thesis2/1.introduction.tex b/thesis2/1.introduction.tex
index 366d13a..74efdbc 100644 (file)
--- a/thesis2/1.introduction.tex
+++ b/thesis2/1.introduction.tex
@@ -62,6 +62,9 @@ the only company in its kind that has such high quality information. The
 \textit{infotainment} is presented via several websites specialized per genre
 or category and some sites attract over $500.000$ visitors per month.
 
+\section{Extracting data from plain text}
+
+
 \section{Information flow}
 The reason why Hyperleap is the only in its kind with the high quality data is
 because Hyperleap spends a lot of time and resources on quality checking, cross

diff --git a/thesis2/3.methods.tex b/thesis2/3.methods.tex
index 5729b79..4fd871d 100644 (file)
--- a/thesis2/3.methods.tex
+++ b/thesis2/3.methods.tex
@@ -1,9 +1,12 @@
 \section{Application overview}
 The backend consists of several processing steps before a crawler specification
-is ready. 
+is ready. These steps are visualized in Figure~\ref{appinternals} where all the
+nodes are important milestones in the process of processing the user data.
+Arrows indicate informatio transfer between these steps. The Figure is a
+detailed explanation of the \textit{Backend} node in Figure~\ref{appoverview}.
 
 \begin{figure}[H]
-       \label{dawg1}
+       \label{appinternals}
        \centering
        \includegraphics[width=\linewidth]{backend.eps}
        \strut\\
@@ -11,40 +14,51 @@ is ready.
 \end{figure}
 
 \section{HTML data}
-Data marked by the user will be returned as a \texttt{POST} http request and is
-basically the information in the description fields accompanied by the raw html
-of the table with the patterns. For every RSS feed entry there is a row in the
-table and every marker is a \texttt{SPAN} element containing the color of the
-text. Within the HTML code of the table markers are placed to make parsing more
-easy and remove the need of an advanced HTML parser. The \texttt{POST} request
-that will be send when the user presses the submit button will not be sent
-asynchronously, the user has to wait for the processing to finish. Because of
-this the user will be immediately notified when the processing has finished
-successfully. The preparation of the data for the \texttt{POST} request is all
-done in Javascript on the user side, the processing of the data in the backend
-is all done server side.
+The raw data from the \textit{Frontend} with the user markings will enter the
+backend as a HTTP \textit{POST} request and consists of several information
+fields extracted from the description boxes in the front and accompanied by the
+raw \textit{HTML} data from the table with the feed entries that contain the
+marking made by the user at the time just before the submit button is pressed.
+Within the \textit{HTML} data of the table markers are placed before sending to
+make the parsing of the tables more easy and remove the need for an advanced
+\textit{HTML} parser. The \textit{POST} request is not send asynchronously,
+this means the user has to wait until the server has processed the request. In
+this way the user will be immediately notified when the processing has finished
+successfully and will be able to review and test the resulting crawler. All the
+data preparation for the \textit{POST} request is done in Javascript and thus
+on the user side, all the processing afterwards is done in the backend and thus
+server side. All the descriptive fields will be transferred to the final
+aggregating dictionary.
 
 \section{Table rows}
-When the backend receives the HTTP POST data it saves all the table HTML data
-and the data from the description fields. The program first extracts the table
-rows using simple pattern matching on the added markers. The table rows are
-processed one at the time, the processing mainly consists of finding the
-\texttt{SPAN} elements, extract the color, remove the elements and save the
-plain text and the location and type of the marking. When the table rows are
-processed a datastructure with all the entries accompanied by their markers
-will go to the next step. All intermediate data will be saved for later use.
+The first conversion step is to extract the rows within from the \textit{HTML}
+data. Because of the table markers this process can be done using very
+rudimentary matching techniques that require very little computational power.
+All the \texttt{SPAN} \textit{HTML} elements have to be found since the marking
+of the user are visualized through colored \texttt{SPAN} elements. To achieve
+this for every row all \texttt{SPAN} elements are extracted, again with simple
+matching techniques, to extract the color and afterwards to remove the element
+to retrieve the original plain text of the \textit{RSS} feed entry. When this
+step is done a data structure containing all the text of the entries together
+with the markings will go to the next step. All original data, namely the
+\textit{HTML} data per row, will be transferred to the final aggregating
+dictionary.
 
 \section{Node lists}
-Every entry from the datastructure is processed to convert them to node-lists.
-A node-list can be seen as a path graph of every character and marking. A path
-graph $G$ is defined as $G=(V,n_1,E,n_i)$ where $V=\{n_1, n_2, \cdots, n_{i-1},
-n_i\}$ and $E=\{(n_1, n_2), (n_2, n_3), ... (n_{i-1}, n_{i})\}$. A path graph
-is basically a graph that is a path of nodes where every node is connected to
-the next on and the last on being final. The transitions between two nodes is
-either a character or a marking. As an example we take the entry \texttt{19:00,
-2014-11-12 - Foobar} and create the corresponding node-lists and make it
-visible in Figure~\ref{nodelistexample}. Characters are denoted with single
-quotes, spaces with an underscore and markers with angle brackets.
+Every entry gotten from the previous step are going to be processing into so
+called node-lists. A node-list can be seen as a path graph of every character
+and marking. A path graph $G$ is defined as $G=(V,n_1,E,n_i)$ where $V=\{n_1,
+n_2, \cdots, n_{i-1}, n_i\}$ and $E=\{(n_1, n_2), (n_2, n_3), ... (n_{i-1},
+n_{i})\}$. A path graph is basically a graph that is a path of nodes where
+every node is connected to the next on and the last on being final. The
+transitions between two nodes is either a character or a marking. As an example
+we take the entry \texttt{19:00, 2014-11-12 - Foobar} and create the
+corresponding node-lists and make it visible in Figure~\ref{nodelistexample}.
+Characters are denoted with single quotes, spaces with an underscore and
+markers with angle brackets. Node-lists are the basic elements from which the
+DAWG will be generated. These node-lists will also be available in the final
+aggregating dictionary to ensure consistency of data and possibility of
+regenerating the data.
 \begin{figure}[H]
        \label{nodelistexample}
        \centering
@@ -52,7 +66,6 @@ quotes, spaces with an underscore and markers with angle brackets.
        \caption{Node list example}
 \end{figure}
 
-
 \section{DAWGs}
 \subsection{Terminology}
 \textbf{Parent nodes} are nodes that have an arrow to the child.\\
@@ -63,18 +76,20 @@ We represent the user generated patterns as DAWGs by converting the node-lists
 to DAWGS. Normally DAWGs have single letters from an alphabet as edgelabel but
 in our implementation the DAWGs alphabet contains all letters, whitespace and
 punctuation but also the specified user markers which can be multiple
-characters in length.
+characters of actual length but for the DAWGs' sake they are one transition in
+the graph.
 
 DAWGs are graphs but due to the constraints we can use the DAWG to check if a
 match occurs by checking if a path exists that creates the word by
-concatenating all the edge labels. The first algorithm to generate DAWGs from
+concatenating all the edge labels while trying to find a path following the
+characters from the entry. The first algorithm to generate DAWGs from
 words was proposed by Hopcroft et al\cite{Hopcroft1971}. It is an incremental
 approach in generating the graph.  Meaning that entry by entry the graph will
 be expanded. Hopcrofts algorithm has the constraint of lexicographical
 ordering. Later on Daciuk et al.\cite{Daciuk2000} improved on the original
 algorithm and their algorithm is the algorithm we used to minimize our DAWGs. A
 minimal graph is a graph $G$ for which there is no graph $G'$ that has less
-paths and $\mathcal{L}(G)=\mathcal{L }(G')$ where $\mathcal{L}(G)$ is the set
+paths and $\mathcal{L}(G)=\mathcal{L}(G')$ where $\mathcal{L}(G)$ is the set
 of all words present in the DAWG. 
 
 \subsection{Algorithm}
@@ -216,6 +231,9 @@ added.
        }
 \end{figure}
 
+\subsection{Minimality of the algorithm}
+
+
 \subsection{Appliance on extraction of patterns}
 The text data in combination with the user markings can not be converted
 automatically to a DAWG using the algorithm we described. This is because the

diff --git a/thesis2/Makefile b/thesis2/Makefile
index 7c7fbef..ad0c690 100644 (file)
--- a/thesis2/Makefile
+++ b/thesis2/Makefile
@@ -1,5 +1,5 @@
 SHELL:=/bin/bash
-VERSION:=0.91
+VERSION:=0.95
 
 all: thesis
 

diff --git a/thesis2/backend.dot b/thesis2/backend.dot
index eef3f17..1acf7a2 100644 (file)
--- a/thesis2/backend.dot
+++ b/thesis2/backend.dot
@@ -2,12 +2,12 @@ digraph {
                rankdir=LR
                q0 [style=invis]
                q1 [style=invis]
-               q0 -> "HTML data"
+               q0 -> "HTML data" [label="From frontend"]
                "HTML data" -> "Table rows"
                "Table rows" -> "Node lists"
                "Node lists" -> "Dawg"
                "Dawg" -> "Dictionary"
                "HTML data" -> "Dictionary" [label="Description fields"]
                "Table rows" -> "Dictionary" [label="Original text"]
-               "Dictionary" -> q1
+               "Dictionary" -> q1 [label="To crawler"]
 }

diff --git a/thesis2/backend.eps b/thesis2/backend.eps
index 65c7bc7..5b6bd39 100644 (file)
--- a/thesis2/backend.eps
+++ b/thesis2/backend.eps
@@ -2,7 +2,7 @@
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diff --git a/thesis2/nodelistexample.dot b/thesis2/nodelistexample.dot
index f4e5f03..5bffaae 100644 (file)
--- a/thesis2/nodelistexample.dot
+++ b/thesis2/nodelistexample.dot
@@ -2,6 +2,7 @@ digraph {
        rankdir=LR;
        n0 [style=invis]
        n9 [shape=doublecircle]
+       n0 -> n1
        n1 -> n2 [label="<time>"]
        n2 -> n3 [label="','"]
        n3 -> n4 [label="'_'"]

diff --git a/thesis2/nodelistexample.eps b/thesis2/nodelistexample.eps
index 61819bb..150122a 100644 (file)
--- a/thesis2/nodelistexample.eps
+++ b/thesis2/nodelistexample.eps
@@ -2,7 +2,7 @@
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