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cleaning up images and some text

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Oliver Kennedy 2018-04-29 00:21:00 -04:00
parent c3c3f39187
commit 23819edfb7
8 changed files with 1634 additions and 45 deletions
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38
sections/4-experiments.tex
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@ -23,11 +23,8 @@ An older version of the dataset that includes a month's trace of SQLite activity
The data that we used in this paper is available by request.
%, and the researchers has to follow the IRB requirements of their institute to be able to use, and publish using this data.
\begin{table}[]
\begin{figure}
\centering
\caption{Dataset}
\label{tab:dataset}
\vspace{-0.2cm}
\begin{tabular}{cc}
\textbf{Application} & \textbf{\# of queries} \\ \hline
Facebook & 245,550 \\
@ -37,7 +34,10 @@ Photos & 165,347
Google Play Services & 3,008,722 \\
Twitter & 27,318 \\ \hline
\end{tabular}
\end{table}
\caption{Example PocketData App Logs}
\label{tab:dataset}
\trimfigurespacing
\end{figure}
%\subsubsection{Dataset 2 - Controlled Dataset}
%\label{sec:controlleddataset}
@ -96,22 +96,32 @@ To achieve this, we designed the following experiments as building blocks to ver
Extracting meaningful patterns from user data is central to a reliable characterization of the smartphone database workload. In this experiment, we aim to show the accuracy of our proposed approach, and test various methods in search of the best performance.
One of the ways of doing it is to show that the method proposed can \emph{predict} the same user's expected workload for a given timeframe. To achieve this, under the assumption that there would be a certain level of monotonicity of the users' weekly smartphone usage, we partitioned the 21 days of query workload created by each user into 3 equal partitions. We cross-validated the accuracy results of our tests by comparing each partition with the other two partition.
Following this approach, we compared 5 different session similarity measures:
% \begin{itemize}
Following this approach, we performed 5 different runs comparison, changing one session similarity method each time:
\begin{itemize}
\item Without Session Identification: We identify each query as a session, and used the query cluster assignments for comparison.
\item Cluster-based Set Difference: We apply our session identification methodology, and perform session clustering by creating the distance matrix through the comparison of query cluster assignments with Jaccard Index.
\item Cluster-based Divergence: We apply our session identification methodology, and perform session clustering by creating the distance matrix through the comparison of query cluster assignment frequencies in each session with JS-Divergence.
\item Feature-based Set Difference: We apply our session identification methodology, and perform session clustering by creating the distance matrix through the comparison of feature sets extracted from each session with Jaccard Index.
\item Feature-based Divergence: We apply our session identification methodology, and perform session clustering by creating the distance matrix through the comparison of feature appearance frequencies in each session with JS-Divergence.
\end{itemize}
\tinysection{Without Session Identification}
We identify each query as a session, and use query cluster assignments for comparison.
\tinysection{Cluster-based Set Difference}
We apply our session identification methodology, and perform session clustering by creating the distance matrix by comparing query cluster assignments with the Jaccard Index.
\tinysection{Cluster-based Divergence}
We apply our session identification methodology, and perform session clustering by creating the distance matrix by comparing query cluster assignment frequencies with JS-Divergence.
\tinysection{Feature-based Set Difference}
We apply our session identification methodology, and perform session clustering by creating the distance matrixby comparing of feature sets extracted from each session with the Jaccard Index.
\tinysection{Feature-based Divergence}
We apply our session identification methodology, and perform session clustering by creating the distance matrix by comparing feature appearance frequencies in each session with JS-Divergence.
% \end{itemize}
\begin{figure}[h!]
\centering
\includegraphics[width=0.45\textwidth]{graphics/WorkloadPredictibility}
\vspace{-0.5cm}
\caption{Workload Prediction Accuracy Comparison}
\caption{Prediction Accuracy Comparison}
\label{fig:averagesimilarity}
\trimfigurespacing
\end{figure}
%We proposed a robust session similarity measure which takes into the account the considerations and constraints that are imposed by the problem domain.

10
sections/4a-sessionidentification.tex
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@ -1,3 +1,4 @@
% -*- root: ../paper.tex -*-
In our framework, a \textit{database session} is a logical unit of user interaction. It spans over a period of time and is comprised of sequential queries. If two sequential queries are more than \textit{t} seconds apart, we consider them to be in different sessions. Parameter \textit{t} is called the Idle Time Tolerance.
Since the number and content of these sessions depend on the idle time parameter \textit{T}, it is important to identify the best idle time. While the idea of a low idle time tolerance might be enticing because it enables us to look into the query log at a more granular level, this does not work well in a mobile setting. In our real-world data set, when the number of user sessions became too high, neighboring sessions started to become very similar to each other. This is because some apps tend to issue the same query repeatedly to the database for various types of activites.
@ -78,9 +79,7 @@ Table \ref{table:exampletable2} represents a sample output of session identifica
{\footnotesize
\begin{table}
\caption{Query log after session identification using automatic idle time selection}
\label{table:exampletable2}
\begin{figure}
\scalebox{0.7}{
\begin{tabular}{EEES{3in}}
% \begin{sideways}\textbf{Session}\end{sideways} & \begin{sideways}\textbf{ID}\end{sideways} & \begin{sideways}\textbf{Time}\end{sideways} & \begin{sideways}\textbf{Query}\end{sideways} \\ \hline
@ -163,7 +162,10 @@ Table \ref{table:exampletable2} represents a sample output of session identifica
\end{lstlisting}} \\
\end{tabular}
}
\end{table}
\caption{Query log after session segmentation}
\label{table:exampletable2}
\trimfigurespacing
\end{figure}
}
\end{example}

14
sections/4b-profiler.tex
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@ -1,3 +1,4 @@
% -*- root: ../paper.tex -*-
In order to be able to perform a similarity assessment among queries, activities, and sessions, we need to be able to extract features out of SQL queries.
Extracting features from a SQL query can be done in many ways. Let's consider the following queries:
@ -63,11 +64,12 @@ where $Q^{t_i}_u$ represents a query $Q^{t_i}$ issued at time $t_i$ by user $u$
The feature-based \textit{session profiles} are created by accumulating
the features of the queries from the beginning to the end of the session as shown in Figure~\ref{fig:entropySessionProfile}.
\begin{figure}[h!]
\begin{figure}
\centering
\includegraphics[width=0.25\textwidth]{graphics/entropy.png}
\caption{Creating a probability distribution based session profile}
\includegraphics[width=0.7\columnwidth]{graphics/entropy.pdf}
\caption{Probability-based session profiling}
\label{fig:entropySessionProfile}
\trimfigurespacing
\end{figure}
%\begin{figure}[h!]
@ -88,11 +90,13 @@ distance = 1 - similarity
where the similarity is the score we get from the methods explained above.
\begin{figure}[h!]
\begin{figure}
\centering
\vspace{2mm}
\includegraphics[width=0.5\textwidth]{graphics/systemoutline}
\caption{Session representations with query clustering}
\caption{Query clustering-based segmentation}
\label{fig:clusterSession}
\trimfigurespacing
\end{figure}
We use hierarchical clustering

44
sections/4c-analyzer.tex
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@ -1,3 +1,4 @@
% -*- root: ../paper.tex -*-
Frequently occuring database user sessions which are similar to each other are useful for summarization. Since they represent one or more parts of logical user activities, frequency of their occurence can be considered proportional to the occurence of recurring user activities in the query log.
Clustering these sessions would group similar user activities. This would enable us to summarize the query log.
% Frequency of occurrence of user sessions in the query log is an important metric. Since a session corresponds to one or more activity being performed together, the clustering of sessions is a logical equivalent to grouping of activities frequently co-occur in a user's workload. \todo{not sure what we are trying to say with these first two sentences, can we revise them to make a clearer point?}
@ -59,21 +60,20 @@ Hence, JS-Divergence provides a symmetric measure for comparing session profiles
We can use either one of these methods to create the pairwise distance matrix of sessions to cluster them. Using the this matrix, the analyzer clusters the similar sessions using hierarchical clustering with the same strategy we defined for query clustering. The output of this clustering directly reveals similar sessions. The sessions that belong to the same group reflects similar querying behaviors of the mobile app. The size of the group shows how frequent that set of activity occurs.
\begin{table}[]
\centering
\caption{Sample distance matrix used for session clustering}
\label{my-table-label}
\vspace{-0.3cm}
%\resizebox{\textwidth}{!}{%
\begin{tabular}{llll}
Sessions & S1 & S2 & S3 \\ \cline{2-4}
\multicolumn{1}{l|}{S1} & \multicolumn{1}{l|}{0} & \multicolumn{1}{l|}{\cellcolor[HTML]{C0C0C0}0.2} & \multicolumn{1}{l|}{\cellcolor[HTML]{000000}{\color[HTML]{000000} 0.9}} \\ \cline{2-4}
\multicolumn{1}{l|}{S2} & \multicolumn{1}{l|}{\cellcolor[HTML]{000000}0.2} & \multicolumn{1}{l|}{0} & \multicolumn{1}{l|}{\cellcolor[HTML]{656565}0.5} \\ \cline{2-4}
\multicolumn{1}{l|}{S3} & \multicolumn{1}{l|}{\cellcolor[HTML]{000000}{\color[HTML]{000000} 0.9}} & \multicolumn{1}{l|}{\cellcolor[HTML]{656565}0.5} & \multicolumn{1}{l|}{0} \\ \cline{2-4}
\end{tabular}%
%}
\end{table}
% \begin{figure}
% \centering
% \vspace{-0.3cm}
% %\resizebox{\textwidth}{!}{%
% \begin{tabular}{llll}
% Sessions & S1 & S2 & S3 \\ \cline{2-4}
% \multicolumn{1}{l|}{S1} & \multicolumn{1}{l|}{0} & \multicolumn{1}{l|}{\cellcolor[HTML]{C0C0C0}0.2} & \multicolumn{1}{l|}{\cellcolor[HTML]{000000}{\color[HTML]{000000} 0.9}} \\ \cline{2-4}
% \multicolumn{1}{l|}{S2} & \multicolumn{1}{l|}{\cellcolor[HTML]{000000}0.2} & \multicolumn{1}{l|}{0} & \multicolumn{1}{l|}{\cellcolor[HTML]{656565}0.5} \\ \cline{2-4}
% \multicolumn{1}{l|}{S3} & \multicolumn{1}{l|}{\cellcolor[HTML]{000000}{\color[HTML]{000000} 0.9}} & \multicolumn{1}{l|}{\cellcolor[HTML]{656565}0.5} & \multicolumn{1}{l|}{0} \\ \cline{2-4}
% \end{tabular}%
% %}
% \caption{Sample distance matrix for session clustering}
% \label{my-table-label}
% \end{figure}
%We calculate $J(S_i,S_j)$ for all pairs of user sessions.
%Now, we start to look for "interesting" user sessions. A high Jaccard similarity score for a pair of user sessions can be interpreted as them being similar to each other, thereby leading the contents to occur more frequently. For a particular user session $w_i$, we would be now looking out for the top K user sessions which are most similar with $w_i$. Calculating the average similarity of $w_i$ with the most similar K sessions $[w_1,w_2..., w_k]$ yields a notion of the importance of $w_i$ in representing the characteristics of the workload represented in the query log. We denote this average similarity for $w_i$ with top K windows as $J_{w_i{avg}}$.
@ -86,11 +86,9 @@ We can use either one of these methods to create the pairwise distance matrix of
\begin{example}[Running example]
Table \ref{table:exampletable3} represents a sample output of session clustering. This query log was introduced in Table \ref{table:exampletable1} as a subset of the actual Facebook app workload. The sessions were identified and sample output was presented in Table \ref{table:exampletable2}. These two sessions represent the clusters of most repetitive sessions and hence, the most commonly occuring user activities. At this point, we have effectively summarized the query log. From prior knowledge about the query log, we can label these clusters with the associated user activities - in this case "Event reminders" and "Messaging". These labels serve as a validation of the session clustering.
{\footnotesize
\begin{table}[h!]
\caption{Query log after session clustering representing most repetitive sessions}
\label{table:exampletable3}
\begin{figure}
{\footnotesize
\scalebox{0.7}{
\begin{tabular}{EEES{3in}}
% \begin{sideways}\textbf{Session}\end{sideways} &
@ -170,10 +168,12 @@ Table \ref{table:exampletable3} represents a sample output of session clustering
\end{lstlisting}
} \\
\end{tabular}}
\vspace{-0.5cm}
\end{table}
}
\caption{Query log after session clustering representing most repetitive sessions}
\label{table:exampletable3}
\trimfigurespacing
\end{figure}
\end{example}
%These atomic units will be used for detecting patterns. They contain one or more subsets of logical user activities. \todo{more description need here}