wörk
Clemens Klug
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%Necessary Information
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\author{Clemens Klug}
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\title{A Framework for the Analysis of Spatial Game Data}
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\subtitle{Ein Analyseframework f\"ur raumbezogene Spieldaten}
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\subtitle{Ein Analyseframework f\"ur raumbezogene Spieldaten\\2. Vortrag}
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%The day of the presentation
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%\date{\today}
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\date{February 28, 2018}
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\titlegraphic{\includegraphics[width=13mm,height=13mm]{image/logo}}
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%\gittrue
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\gittrue
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\newcommand\meta{../meta}
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\input{\meta/config/commands}
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\newcommand{\backupbegin}{
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@ -94,162 +100,6 @@ compress
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%%%%%%%%%% Content starts here %%%%%%%%%%
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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\section{Location based Games}
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\begin{frame}{Location based Games}
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\framesubtitle{Put the 'fun' in education}
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\begin{columns}
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\column{0.55\linewidth}
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Intersection of GIS and gaming technology\footnotemark
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\begin{itemize}
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\item Game actions tied to real-world spatial places ('Break the magic circle')%TODO citation
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\item Locomotion as essential game part
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\item Trade-offs between board and race style games\footnotemark
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\item Foster recognition of environment
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\item Focus through game related tasks
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\end{itemize}
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\column{.45\linewidth}
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\image{.5\textwidth}{gg2}{Geogame map view}{img:gg2}
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\end{columns}
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\addtocounter{footnote}{-1}
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\footcitetext{Ahlqvist2018}\stepcounter{footnote}
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\footcitetext{1705427}
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\end{frame}
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\begin{frame}{Research with location based games}
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\begin{columns}
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\column{0.35\linewidth}
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\begin{itemize}
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\item Define mission statement
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\item Build statistical framework
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\item Questionnaires
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\begin{itemize}
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\item Pre
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\item Post
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\end{itemize}
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\item {[Data entry]}
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\item Statistical processing
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\end{itemize}
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\column{.6\linewidth}
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\image{\textwidth}{biodiv-schaal}{Statistical framework for BioDiv2Go\footnotemark}{img:biodiv-schaal}
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\end{columns}
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%\addtocounter{footnote}{-1}
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\footcitetext{Schaal2017}%\stepcounter{footnote}
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\end{frame}
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\section{Goal definition}
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\begin{frame}{Goal definition}
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\framesubtitle{A Framework for the Analysis of Spatial Game Data}
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\begin{itemize}
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\item Framework for analysis
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\item Two target groups:
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\begin{itemize}
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\item Expert users/researchers
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\item Staging/designing staff
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\end{itemize}
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\item Integration of external data (questionnaire results)
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\item Adaptable for multiple games
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\end{itemize}
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\end{frame}
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\begin{frame}{Components}
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Prerequisites: Game log
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\begin{itemize}
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\item Creation
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\item Storage
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\end{itemize}
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New components
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\begin{itemize}
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\item Per-game importer (Web client, File loader, …)
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\item Analyzer modules (number crunching)
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\item Output \& Visualization (CSV, [Geo]JSON, KML, Graphs, …)
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\item Interface (Configuration)
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\item Cross-game comparisons
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\end{itemize}
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\end{frame}
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\section{State of research}
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\frame
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{
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\tableofcontents[sectionstyle=show/hide,hideothersubsections]
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}
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\subsection{Log processing}
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\begin{frame}{Modern log processing stacks}
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Technologies: Collection, Time-series databases, Frontend
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\begin{itemize}
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\item ELK (Elastic search, Logstash, Kibana)\autocite{andreassen2015monitoring} \autocite{yang2016aggregated} \autocite{steinegger2016analyse} \autocite{sanjappa2017analysis}
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\item Collectd, Influx DB, Grafana \autocite{komarek2017metric}
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\item …
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\end{itemize}
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\begin{itemize}
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\item[+] widely deployed
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\item[+] powerful query languages %TODO example
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\item mainly web/container/hardware monitoring
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\item[-] spatial analysis: heavily anonymized
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\item[-] fast-paced environment
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\end{itemize}
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\end{frame}
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\subsection{Pedestrian traces}
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\begin{frame}{Analyzing pedestrian movement}
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\framesubtitle{… based on GPS logs}
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\begin{itemize}
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\item GPS overestimates systematically \autocite{Ranacher_2015}
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\item GPS is a suitable instrument for spatio-temporal data\autocite{van_der_Spek_2009}
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\item Activity mining \autocite{Gong_2014}
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\begin{itemize}
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\item Speed-based Clustering \autocite{ren2015mining}
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%\item \autocite{Ferrante_2016} % closed access
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\item Machine Learning \autocite{pattern_recog} %TODO
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\end{itemize}
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\item E.g.: Improve tourist management \autocite{tourist_analysis2012}
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\end{itemize}
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\end{frame}
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\begin{frame}{Heatmap}
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\image{.81\textwidth}{strava}{Heatmap: Fitnesstracker\autocite{strava}}{img:strava}
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\end{frame}
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\begin{frame}{Space-time cube}
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\image{.72\textwidth}{space-time}{Space-time cube examples\autocite{bach2014review}}{img:spacetime}
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\end{frame}
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\begin{frame}{Trajectory patterns}
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\image{\textwidth}{traj-pattern}{Flock and meet trajectory pattern\autocite{jeung2011trajectory}}{img:traj-pattern}
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\end{frame}
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\begin{frame}{Generalization}
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\image{\textwidth}{generalization}{Trajectories and generalizations with varying radius parameter \autocite{adrienko2011spatial}}{img:generalization}
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\end{frame}
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\subsection{Analyzing games}
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\begin{frame}{Analyzing games}
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\begin{itemize}
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\item there's more than heatmaps
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\item combine position with game actions
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\item identify patterns, balancing issues
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\item manual processes %\citetitle{Drachen2013}\citetitle{AHLQVIST20181}
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\end{itemize}
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\vspace{-42pt}
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%\image{.5\textwidth}{game-an}{chat logs with players location \autocite{Drachen2013}}{img:chatlogs}
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%\image{.5\textwidth}{ac3-death}{identify critical sections \autocite{Drachen2013}}{img:ac3death}
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\twofigures{0.5}{game-an}{Chat logs with players location}{img:chatlogs}{ac3-death}{Identify critical sections}{img:ac3death}{Game analytics \cite{Drachen2013}}{fig:gameanal}
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\end{frame}
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%\subsection{Location based games}
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\subsection{Summary}
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\begin{frame}{Summary}
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\begin{itemize}
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\item Log processing: Powerful stacks
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\item Movement analysis: Large field already explored (GPS influence, Patterns, Behavior recognition, …)
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\item Track rendering: Track (with attributes), Space-time cube, Heatmap, …
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\item Spatial analysis of digital games with GIS
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\item Analysis of location based games: Laborious manual process
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\end{itemize}
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\end{frame}
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\section{Solution approach}
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\frame
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{
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@ -273,12 +123,15 @@ Technologies: Collection, Time-series databases, Frontend
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\end{frame}
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\subsection{Architecture}
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\begin{frame}{Experiment: Kibana}
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\image{.85\textwidth}{kibana}{Game trace in Kibana}{img:kibana}
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\end{frame}
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%\begin{frame}{Experiment: Kibana}
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%\image{.85\textwidth}{kibana}{Game trace in Kibana}{img:kibana}
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%\end{frame}
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\begin{frame}{Experiment: Kibana}
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\image{.85\textwidth}{kibana2}{Game trace in Kibana}{img:kibana2}
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\end{frame}
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\begin{frame}{Experiment: Grafana}\hspace{.3\textwidth}a\\[-24pt]
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\image{.85\textwidth}{grafana}{Side project: Weather station with Grafana}{img:grafana}
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\end{frame}
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\begin{frame}{Architecture}
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\begin{itemize}
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@ -295,8 +148,8 @@ Technologies: Collection, Time-series databases, Frontend
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\end{itemize}
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\item Reduce: Collect summaries from analyzers
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\begin{itemize}
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\item Rendering
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\item Post-processing, Comparison, …
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\item Rendering
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\end{itemize}
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\end{itemize}
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\end{frame}
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\image{\textwidth}{../../ThesTeX/images/map-reduce.pdf}{Data flows}{img:flow}
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\end{frame}
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\section{Outlook: Implementation}
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\section{Implementation}
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\subsection{Technologies}
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\begin{frame}{Implementation}
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\begin{columns}
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\column{0.55\linewidth}
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@ -313,7 +167,7 @@ Technologies: Collection, Time-series databases, Frontend
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\begin{itemize}
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\item Python (3.6)
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\item Standalone library/CLI tool
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\item Web based configuration/Runner/API (Flask)
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\item Web based configuration/Runner/API (Flask, Celery)
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\end{itemize}
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Rendering
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\begin{itemize}
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@ -335,24 +189,53 @@ Technologies: Collection, Time-series databases, Frontend
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\end{columns}
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\end{frame}
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\begin{frame}{Configuration \& results}
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\subsection{Microservice Composition}
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\begin{frame}{Microservice Composition}
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\framesubtitle{Docker everything!}
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\begin{columns}
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\column{0.45\linewidth}
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\begin{itemize}
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\item Analysis framework: Celery
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\item User interface: Flask
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\item Result server: Nginx
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\item Connection Flask - Celery: Redis
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\item Public frontend: Traefik (external)
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\end{itemize}
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\column{0.525\linewidth}
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\image{\textwidth}{../../ThesTeX/images/architecture}{Service structure}{img:microservices}
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\end{columns}
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\end{frame}
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\subsection{Classes}
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\begin{frame}{Analysis}
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\twofigures{0.5}{code/analyzer}{Analyzer interface}{code:anif}{code/mask-spatials}{Sample analyzer}{code:mask}{Analyzer stub and implementation}{fig:pyan}
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\end{frame}
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\begin{frame}{Result}
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\image{\textwidth}{code/result}{Result class}{code:result}
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\end{frame}
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\begin{frame}{Render}
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\twofigures{0.5}{code/render}{Render interface}{code:reif}{code/render-board}{Sample render}{code:rebo}{Render stub and implementation}{fig:pyre}
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\end{frame}
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\subsection{Configuration}
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\begin{frame}{Configuration \& result}
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\twofigures{0.5}{oeb-kml}{Analyzer configuration}{img:oebkml}{oeb-ge}{Result visualized}{img:oebge}{Example: Generate KML tracks (BioDiv2Go; Oberelsbach2016)}{fig:oeb2016}
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\end{frame}
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\begin{frame}{ActivityMapper}
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\image{.7\textwidth}{track-fi}{Combined screen activity and spatial progress}{img:trackfi}
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\end{frame}
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\section{Outlook: Evaluation}
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%TODO
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\begin{frame}{Graphs}
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\begin{columns}
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\column{0.45\linewidth}
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\image{\textwidth}{simu-retries}{Experimentational rounds}{img:retries}
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\column{0.45\linewidth}
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\image{\textwidth}{speed}{Speed distribution}{img:speed}
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\end{columns}
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\end{frame}
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\begin{frame}{Graphs}
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\image{.9\textwidth}{time-rel}{Time distribution}{img:time}
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\begin{frame}{Evaluation}
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\begin{itemize}
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\item Analyse other geogames
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\item Describe effort
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\item ?
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\item Profit
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\end{itemize}
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\end{frame}
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\normalsize
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\end{frame}
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\appendix
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\backupbegin
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\begin{frame}{Graphs}
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\begin{columns}
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\column{0.45\linewidth}
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\image{\textwidth}{simu-retries}{Experimentational rounds}{img:retries}
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\column{0.45\linewidth}
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\image{\textwidth}{speed}{Speed distribution}{img:speed}
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\end{columns}
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\end{frame}
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\begin{frame}{Graphs}
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\image{.9\textwidth}{time-rel}{Time distribution}{img:time}
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\end{frame}
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\backupend
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\end{document}
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@ -30,7 +30,8 @@ Additionally, this requires the users to master the query language before any re
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By building a custom plugin, extension, or modified version, it is possible to circumvent this obstacle.
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However, the fast-paced environment of the industry either requires a constant effort of keeping pace, or results in an outdated system rather quickly.\footnote{E.g. the next major release Kibana v6.0.0\footnote{\url{https://github.com/elastic/kibana/releases/tag/v6.0.0}} was released about a year after Kibana v5.0.0\footnote{\url{https://github.com/elastic/kibana/releases/tag/v5.0.0}}. However, the previous major version seems to receive updates for about an year, too.}
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\subsection{Evaluation Grafana}
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\image{\textwidth}{grafana-metrics}{Configuring a graph in Grafana}{img:graphana}
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\subsection{Conclusion}
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After all, the monitoring solutions are no perfect match for this special use case.
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The privacy concerns vital in web monitoring prohibit detailed spatial analyzes, the query languages can restrict some questions, and custom extensions require constant integration effort.
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@ -43,7 +44,7 @@ The default Kibana worchbench does not qualify for this, a custom interface coul
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\section{Architectural Design}
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%TODO: map-reduce
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\subsection{Overview}
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Based on the learnings above, a custom stack seems like a feasible alternative:
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\begin{itemize}
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