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ThesTeX/TODO
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ThesTeX/TODO
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einleitung:
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* biodiv2go: zu speziell => nach 2 verschieben "orientierung an… , aber flexibel"
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2:
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* #teacher: 1 game
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* #research: \inf games
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3
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* #adimistrative doppeldeutig
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4:
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* (modularität herausarbeiten, stream/pipelining)
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5:
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* webui
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+ screenshot
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+ ergebnis
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+ activitymapper
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7:
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* keypoints
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* modular
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* api / unabhängig
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* activitymapper:
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* track + bilder parallel
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@ -1,6 +1,9 @@
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\section{Location based Games: Put the 'fun' in education}
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In this thesis, a framework for the analysis of spatial game data is developed.
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This game data is collected during the game sessions and stored in log files.
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The following chapters describe the basics of the development process.
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\section{Location based Games: Put the `fun' in education}
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Spatial games, also known as location based games, are at the intersection of GIS and gaming technology \cite{Ahlqvist2018}.
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With game actions tied to real-world spatial places, this genre breaks the magic circle of games: they are embedded into the environment and the boundary between game and non-game are vanishing \cite{montola2009games}.
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As they feature locomotion as an essential game part, a focus on certain aspects of the environment can be achieved by game related tasks.
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@ -15,22 +18,5 @@ With a fine tuned setup of educational content, game elements and integration of
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\autoref{img:gg2} shows the map overview of such a game: FindeVielfalt Simulation\furl{https://biodivlb.jimdo.com/english-1/project-finde-vielfalt/finde-vielfalt-simulation/}\!.
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Located in an orchard, the blue dots are caches tied to game actions.
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To proceed in the games narrative story, the caches are to be completed.
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The players have to complete a task with context of the caches' location.
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The players have to complete a task within the context of the caches' location.
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\image{.5\textwidth}{../../PresTeX/images/gg2}{Geogame map view}{img:gg2}
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\section{Research with location based games}\label{sec:gg-res}
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Usually, when the effectiveness of location based educational games is to be measured, the following pattern is applied:
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After a mission statement has been defined and approved, a fitting statistical framework has to be developed.
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Based on such a framework, questionnaires have to be derived.
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As some metrics cannot be retrieved directly from the questionnaires answers, the statistical framework needs to considers these and consider measurable information to derive the original metric from.
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The finished and for alignment with the mission statement approved questionnaires are then applied at field test with users from the target groups.
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Each field test consists of an upstream questionnaire, a pass of the location based game and a final round of questionnaires.
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After an data entry step for paper-based questionnaires, the raw results are fed into the statistical framework implemented in a statistical processing software to retrieve the final results.
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\cite{Schaal2017} describes this development in the context of the BioDiv2Go project.
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\autoref{img:biodiv-schaal} shows the resulting statistical framework for the valuing of biodiversity as target variable of the location based geogame developed in the BioDiv2Go project.
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\image{\textwidth}{../../PresTeX/images/biodiv-schaal}{Statistical framework for BioDiv2Go\cite{Schaal2017}}{img:biodiv-schaal}
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@ -1,7 +1,25 @@
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\autoref{sec:logproctheo} shows the current state of tools and processes for managing large volumes of log and time series data.
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In \autoref{sec:gg-res} example the involvement of location based games in the research field is reviewed.
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Covering the basic data aggregation, \autoref{sec:logproctheo} shows the current state of tools and processes for managing large volumes of log and time series data.
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An overview of the field of pedestrian track analysis is located in \autoref{sec:pedest}.
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Finally, in \autoref{sec:gametheo} the connection of spatial analysis and digital game optimizations is showcased.
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\section{Research with location based games}\label{sec:gg-res}
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\cite{Schaal2017} describes the evaluation of a location based game.
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To measure the effectiveness of the game, the following pattern is applied:
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After a mission statement has been defined and approved, a fitting statistical framework has to be developed.
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Based on such a framework, questionnaires have to be derived.
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As some metrics cannot be retrieved directly from the questionnaires answers, the statistical framework needs to considers these and consider measurable information to derive the original metric from.
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The finished and for alignment with the mission statement approved questionnaires are then applied at field test with users from the target groups.
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Each field test consists of an upstream questionnaire, a pass of the location based game and a final round of questionnaires.
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After an data entry step for paper-based questionnaires, the raw results are fed into the statistical framework implemented in a statistical processing software to retrieve the final results.
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\autoref{img:biodiv-schaal} shows the resulting statistical framework for the valuing of biodiversity as target variable of the location based geogame developed in the BioDiv2Go project.
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\image{\textwidth}{../../PresTeX/images/biodiv-schaal}{Statistical framework for BioDiv2Go\cite{Schaal2017}}{img:biodiv-schaal}
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\section{Log processing}\label{sec:logproctheo}
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System administrators and developers face a daily surge of log files from applications, systems, and servers.
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For knowledge extraction, a wide range of tools is in constant development for such environments.
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@ -58,13 +58,13 @@ While the development of a custom stack requires a lot of infrastructural work t
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With the requirements from \autoref{sec:require} and the learnings from log processing evaluations in mind, a modular processing pipeline depicted in \autoref{img:flowchart} allows for a configurable solution.
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It comprises the stages of input, analysis and rendering.
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With interfaces defined between the stages, this approach allows the exchange of single modules without affecting the remaining pipeline.
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\image{\textwidth}{flowchart.pdf}{Modular processing pipeline}{img:flowchart}
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\image{.75\textwidth}{flowchart.pdf}{Modular processing pipeline}{img:flowchart}
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\subsection{Overview}
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An architectural approach surrounding the processing pipeline is visualized in \autoref{img:solution}.
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It outlines three main components of the project: Two user facing services (Web \& CLI / API), and an analysis framework.
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The interfaces (Web and CLI/API) for both target groups (see \autoref{sec:require}) are completely dependent on the analysis framework at the core.
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\image{\textwidth}{solution.pdf}{Architecture approach}{img:solution}
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\image{.75\textwidth}{solution.pdf}{Architecture approach}{img:solution}
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The following sections describe each of those components.
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\subsection{Analysis Framework}
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@ -125,7 +125,7 @@ Therefore, an additional log providing server was created to allow access to the
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Clients can have arbitrary amounts of options, as all fields in the JSON settings file are passed through (see \autoref{img:oebkml}, section "source").
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\subsection{Web Interface}\label{sec:web}
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\subsection{Web Interface for prepared results}\label{sec:web}
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The selector package holds a Flask\furl{http://flask.pocoo.org/} app for an web interface for non-expert users.
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It utilizes the provided clients (see \autoref{sec:source}) for authentication, and gives users the following options:
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\begin{itemize}
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@ -155,6 +155,13 @@ The link \emph{create new analysis} leads to the configuration menu for new anal
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It lists the game logs available for the logged in user, and offers a selection of the predefined analysis configurations.
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With a given name, it is easy to identify the results for each analysis run in the result overview page.
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\subsection{Result interface}
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Accompanying the Web interface above is the result interface.
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Here, results of the analysis runs issued in the Web interface are displayed to the users.
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\autoref{img:trackfi} shows a result by example: The combination of spatial positions of players and the screen activity.
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\image{\textwidth}{../../PresTeX/images/track-fi}{ActivityMapper: Combined screen activity and spatial progress}{img:trackfi}
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\subsection{Task definition}\label{sec:tasks} in the \texttt{package} provides tasks available for execution.
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This package is the interface for celery\furl{http://www.celeryproject.org/} workers and issuers.
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The key point is the task \texttt{analyze} to start new analysis runs.
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@ -250,4 +257,4 @@ The advantage of docker-compose is the definition of all images, volumes and net
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When a scenario with high load occurs, this definition allows for simple scaling.
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To create more celery worker nodes, issuing the command \textit{docker-compose scale worker=8} suffices to create 8 worker containers running in parallel.
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\image{\textwidth}{architecture.pdf}{Service composition overview}{img:arch}
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\image{.75\textwidth}{architecture.pdf}{Service composition overview}{img:arch}
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\section{Review}
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As shown in \autoref{sec:eval}, the proposed framework (see \autoref{sec:solution}) and its implementation (see \autoref{sec:implementation}) deliver what \autoref{sec:scope} asked for regarding the portability aspect.
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With the web interface depicted in \autoref{app:webif}, it is possible for non-expert users to generate pre-defined reports, while researchers can dive into the API of the framework either as preprocessing step or integrated into a larger project.
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\subsection{Modular framework}
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Given the lean framework core, the development of new analyzers and rendering target is encouraged.
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This is backed by the focus on a standalone application instead of extensions to log processing systems struggling with spatial data in the required resolution.
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As experienced in \autoref{sec:eval}, a change in the import stage of the processing pipeline is completely unnoticed in the other parts.
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The same is true for the addition or modification of analyzering or rendering functionality.
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\subsection{Web UI}
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With the web interface depicted in \autoref{app:webif}, it is possible for non-expert users to generate pre-defined reports, while researchers can dive into the API of the framework either as preprocessing step or integrated into a larger project.
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The web ui also gives direct access to the results for the non-expert users.
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\subsection{Results}
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Th selection of rendered results in \autoref{img:oebkml}, \ref{img:oebge}, \ref{img:retries}, \ref{img:trackfi}, \ref{img:time} showcases the already possible descriptive analysis capabilities.
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\autoref{img:trackfi} features a map view accessible through a browser, which aligns the active screen content of the mobile device with the spatial track.
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\image{\textwidth}{../../PresTeX/images/oeb-ge}{Result visualized}{img:oebge}
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\image{\textwidth}{../../PresTeX/images/simu-retries}{Experimentational rounds}{img:retries}
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\image{\textwidth}{../../PresTeX/images/track-fi}{ActivityMapper: Combined screen activity and spatial progress}{img:trackfi}
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\image{\textwidth}{../../PresTeX/images/speed}{Aggregated speed distribution of four game fields}{img:speed}
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\image{\textwidth}{../../PresTeX/images/time-rel}{Time distribution of game sessions overview of four game fields}{img:time}
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@ -16,5 +16,5 @@
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\chapter{Portability evaluation of the analysis framework}\label{sec:eval}
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\input{content/5-evaluation}
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\chapter{Discussion and outlook}
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\chapter{A modular framework: Discussion and outlook}
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\input{content/6-discussion}
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@ -104,9 +104,9 @@
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% Stichwortverzeichnis soll im Inhaltsverzeichnis auftauchen
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% Sprungmarke mit Phantomsection korrigiert
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\phantomsection%
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\addcontentsline{toc}{chapter}{Index}%
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%\addcontentsline{toc}{chapter}{Index}%
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% Stichwortverzeichnis endgueltig anzeigen
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\printindex%
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%\printindex%
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\appendix
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