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ThesTeX/.gitignore vendored
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.prepared .prepared
# nano swap files # nano swap files
*.swp *.swp
*.lol

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ThesTeX/code/biogames/Dockerfile Normal file
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FROM alpine:edge
RUN set -ex &&\
echo "http://dl-cdn.alpinelinux.org/alpine/edge/testing/" >> /etc/apk/repositories && \
apk upgrade --update &&\
apk add --update nodejs proj4-dev \
geos-dev \
py-geos \
gdal \
gdal-dev \
py-gdal \
py-psycopg2 \
jpeg-dev \
postgresql-dev \
bash \
py-virtualenv \
gcc \
zlib-dev \
python2-dev \
libc-dev \
sqlite-dev \
postgresql-client
ADD ["start.sh", "/"]
VOLUME ["/biogames", "/venv"]
EXPOSE 8000
ENTRYPOINT ["/start.sh"]
CMD ["runserver", "0.0.0.0:8000"]

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ThesTeX/code/biogames/docker-compose.yml Normal file
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version: "3"
services:
web:
build: ./docker2
image: biogames:python2
volumes:
- ./biogames:/biogames
- ../gamefield/:/gamefield
- ./venv_web/:/venv/
- ./gg-data/files/:/biogames/files/
- ./gg-data/media/:/biogames/media/
restart: on-failure:5
depends_on:
- db
networks:
- traefik_net
- default
labels:
- "traefik.enable=true"
- "traefik.port=8000"
- "traefik.docker.network=traefik_net"
- "traefik.http.frontend.rule=Host:biogames.potato.kinf.wiai.uni-bamberg.de"
celery:
image: biogames:python2
volumes:
- ./biogames:/biogames
- ./venv_celery/:/venv/
- ./gg-data/files/:/biogames/files/
- ./gg-data/media/:/biogames/media/
restart: on-failure:5
command: ["celery", "worker"]
depends_on:
- db
db:
image: docker.clkl.de/postgis:9.6-alpine
volumes:
- ./postgres/:/var/lib/postgresql/data
environment:
- POSTGRES_PASSWORD=secret
restart: on-failure:5
networks:
traefik_net:
external:
name: traefik_net

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ThesTeX/code/biogames/start.sh Executable file
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#!/bin/bash
if [ ! -f /venv/bin/activate ]; then
virtualenv /venv;
fi;
source /venv/bin/activate;
LIBRARY_PATH=/lib:/usr/lib pip install -qr /biogames/requirements.txt;
until pg_isready -qh db; do
echo "Waiting for Postgres...";
sleep 1;
done
cd /biogames;
/biogames/manage.py $@

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ThesTeX/code/kibana-docker-compose.yml Normal file
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version: '2'
services:
kibana:
image: docker.elastic.co/kibana/kibana:6.2.1
volumes:
- ./kibana.yml:/usr/share/kibana/config/kibana.yml
environment:
- SERVER_HOST=0.0.0.0
- ELASTICSEARCH_URL=http://elastic:9200
networks:
- default
- traefik_net
labels:
- "traefik.enable=true"
- "traefik.docker.network=traefik_net"
elastic:
image: docker.elastic.co/elasticsearch/elasticsearch-basic:6.2.1
networks:
traefik_net:
external:
name: traefik_net

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version: "3"
services:
traefik:
image: traefik:1.5
command: --logLevel="ERROR" --docker.domain="potato.kinf.wiai.uni-bamberg.de"
volumes:
- /var/run/docker.sock:/var/run/docker.sock
ports:
- 80:80
networks:
- net
networks:
net:
driver: bridge

33
ThesTeX/content/0-introduction.tex
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\section{Auswertung ortsbezogener Spiele}
\section{Location based Games}
\subsection{Put the 'fun' in education}
Intersection of GIS and gaming technology\cite{Ahlqvist2018}
\begin{itemize} \begin{itemize}
\item geogames \item Game actions tied to real-world spatial places ('Break the magic circle')%TODO citation
\item datenspuren, logs \item Locomotion as essential game part
\item erkenntnisgewinn aus spatial logs \item Trade-offs between board and race style games\cite{1705427}
\item geogame analyse (z.b. paper mit auswertung von gg) \item Foster recognition of environment
\item feedback loop: desingers/stagers \item Focus through game related tasks
\end{itemize}
\image{.5\textwidth}{../../PresTeX/images/gg2}{Geogame map view}{img:gg2}
\subsection{Research with location based games}
\begin{itemize}
\item Define mission statement
\item Build statistical framework
\item Questionnaires
\begin{itemize}
\item Pre
\item Post
\end{itemize}
\item {[Data entry]}
\item Statistical processing
\end{itemize} \end{itemize}
Beispiel für Auswertung mit Fragebögen vorher/nachher: \cite{Schaal2017} \image{\textwidth}{../../PresTeX/images/biodiv-schaal}{Statistical framework for BioDiv2Go\cite{Schaal2017}}{img:biodiv-schaal}
TODO \cite{Ahlqvist2018} %TODO

58
ThesTeX/content/1-scope.tex
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@ -1,37 +1,29 @@
\label{sec:scope}
\section{Analyseframework} \subsection{Goal definition}
A Framework for the Analysis of Spatial Game Data
\begin{itemize} \begin{itemize}
\item built-in analysis=> fixed implementation, no cross-game evaluations (independant results/methods etc) \item Framework for analysis
\item framework \item Two target groups:
\begin{itemize} \begin{itemize}
\item create + store game log \item Expert users/researchers
\item per-game importer \item Staging/designing staff
\item analyzer modules crunch numbers \end{itemize}
\item output/visualization \item Integration of external data (questionnaire results)
\item interface \item Adaptable for multiple games
\item cross-game comparisons
\end{itemize}
\end{itemize}
\section{scope}
todo
\begin{itemize}
\item framework for analysis
\item expert users/researcher vs. staging/designing
\item integration von externen daten (fragebögen => CSV o.ä.)
\item adaptable for multiple games
\end{itemize}
not todo
\begin{itemize}
\item interpretation
\item fragebögen
\end{itemize} \end{itemize}
steps \subsection{Components}
\begin{enumerate} Prerequisites: Game log
\item import data \begin{itemize}
\item analyze data \item Creation
\item store/render/… results \item Storage
\end{enumerate} \end{itemize}
New components
\begin{itemize}
\item Per-game importer (Web client, File loader, …)
\item Analyzer modules (number crunching)
\item Output \& Visualization (CSV, [Geo]JSON, KML, Graphs, …)
\item Interface (Configuration)
\item Cross-game comparisons
\end{itemize}

293
ThesTeX/content/2-state-of-the-art.tex
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@ -1,291 +1,2 @@
\begin{itemize} \input{content/2.0-collection}
\item Sensing Human Activity: GPS Tracking \cite{van_der_Spek_2009} %/home/agp8x/Downloads/sensors-09-03033.pdf \input{content/2.1-text}
\\\begin{itemize}
\item 2009
\item evaluate the sensory qualities that GPS technology offers for researching measures of urban quality
\item value of GPS as a sensor technology in urban studies
\end{itemize}
\item Exploring Patterns of Movement Suspension in Pedestrian Mobility \cite{GEAN:GEAN818} %/home/agp8x/Downloads/Exploring_patterns_of_movement_suspension_in_pedestrian_mobility.pdf
\\\begin{itemize}
\item 2011
\item explore patterns of movement suspension
\item classify the movement vectors and identify the location of movement suspension patterns
\item The results have shown that most of the detected patterns formed spatial clusters located around places that represent some attraction in the context in which the pedestrians were moving. For the players in the urban mobile game, suspension patterns were located at the checkpoints and a-priori known locations of gaming events.
\end{itemize}
\item Using location-based tracking data to analyze the movements of city tourists \cite{Modsching:2008:1098-3058:31} %/home/agp8x/Downloads/fulltext.pdf
\\\begin{itemize}
\item 2008
\item spatial distribution of visitors in various areas is visualized and analyzed
\item distribution of walking speeds, areas of slowdown are identified and subsequently clustered into activity areas
\end{itemize}
\item GPS in Pedestrian and Spatial Behaviour Surveys \cite{nielsen2004gps} %/home/agp8x/Downloads/TSN_HHH_Cities_for_people_2004.pdf
\\\begin{itemize}
\item 2004
\item possibilities in spatial behaviour and pedestrian-surveys with GPS and electronic questionnaires
\end{itemize}
\item Monitoring pedestrian spatio-temporal behaviour \cite{millonig2007monitoring} %/home/agp8x/Downloads/paper03millonig.pdf
\\\begin{itemize}
\item 2007
\item observation and interpretation of pedestrian walking patterns and route decision behaviour
\end{itemize}
\item Analysis of Tourist Behavior Based on Tracking Data Collected by GPS \cite{tourist_analysis2012} %12.pdf
\\\begin{itemize}
\item 2012
\item a user-friendly system that integrates the tracking of visitor spatial activity, the elaboration of such data according to structured formats, and their visualization through a webbased viewer
\item design and validation of this comprehensive methodology, and the early results from a test application
\end{itemize}
\item Spatial Game Analytics \cite{Drachen2013} %/home/agp8x/ownCloud/uni/ma/Neuer Ordner/978-1-4471-4769-5_17.pdf
\\\begin{itemize}
\item 2013
\item Introduction to spatial game analytics and the current state-of-the-art in games development and games research.
\item Advice and ideas on how to get started with spatial analysis of behavioral game telemetry data
\item recommendations:
\begin{verbatim}
Dankoff, J. (2011, September 12). Game telemetry with playtest DNA on assassin’s creed. The engine room. URL: http://engineroom.ubi.com/game-telemetry-with-playtest-dna-on-assassins-creed/
Demers, N. (2008). Fundamentals of geographical information systems. URL: http://www.amazon.com/Fundamentals-Geographical-Information-Systems-Michael/dp/0470129069
Drachen, A., & Canossa, A. (2011). Evaluating motion: Spatial user behaviour in virtual environments. URL: http://andersdrachen.files.wordpress.com/2011/01/05_drachen_ijart.pdf
Hoobler, N., Humphreys, G., & Agrawala, M. (2004). Visualizing competitive behaviors in multiuser virtual environments . URL: http://www.cs.virginia.edu/~gfx/pubs/lithium/
Houghton, S. (2011). Balance and fl ow maps . URL: http://altdevblogaday.com/2011/06/01/balance-and-flow-maps-2/
Kennerly, K. (2003). Better game design through data mining . URL: http://www.gamasutra.com/view/feature/2816/better_game_design_through_data_.php
Pruett, C. (2010). Hot failure: Tuning gameplay with simple player metrics. URL: http://www.gamasutra.com/view/feature/6155/hot_failure_tuning_gameplay_with_.php?print=1
Thompson, C. (2007). Halo 3: How Microsoft labs invented a new science of play. URL: http://www.wired.com/gaming/virtualworlds/magazine/15-09/ff_halo
Zoeller, G. (2011). MMO rapid content iteration. URL: http://gdc.gulbsoft.org/
\end{verbatim}
same book:
\begin{verbatim}
18 Visual Game Analytics ............................................................................ 403
Ben Medler
19 Visual Analytics Tools – A Lens into Player’s
Temporal Progression and Behavior ..................................................... 435
Magy Seif El-Nasr, André Gagné, Dinara Moura, and Bardia Aghabeigi
\end{verbatim}
\item -----
\item beloved, but basic: heatmap
\item spatial contexts in computer games, adapting GIS technology
\end{itemize}
\item GeoGame analytics – A cyber-enabled petri dish for geographic modeling and simulation \cite{AHLQVIST20181} %/home/agp8x/ownCloud/uni/ma/Neuer Ordner/1-s2.0-S0198971517304234-main.pdf
\\\begin{itemize}
\item 2018
\item we present the emerging area of Spatial Game Analytics that provides an uncharted area for data-intensive geospatial scenario analysis
\item Exploratory GeoGame analytics to mine spatial behavior of players, identify how variations in the rules and varying locations affect the simulation outcomes.
\item -----
\item manual process: \textit{In our subsequent analysis we first converted our game database to
ArcMap data in order to link the parcel map with game play attributes.
We then used ArcMap primarily as a visual exploration tool to display
parcels and related data, like yield, parcel use, etc., on those parcels.
We also performed some exploratory data analysis in SPSS where our
focus was to look for correlations between variables in the game.}
\end{itemize}
\end{itemize}
\section{Metriken sammeln}
\begin{itemize}
\item Geogames: Designing Location-Based Games from Classic Board Games\cite{1705427}
\\\begin{itemize}
\item …
\end{itemize}
\item … \cite{heinz2015agent}
\\\begin{itemize}
\item …
\end{itemize}
\item … \cite{schlieder2005geogames}
\\\begin{itemize}
\item …
\end{itemize}
\item locomotion-action %TODO citation Reducing Location Overhead in Educational Geogames
\\\begin{itemize}
\item …
\end{itemize}
\end{itemize}
\section{umgang mit trajektorien}
\begin{itemize}
\item Computing with Spatial Trajectories \cite{zheng2011computing} %10.1007%2F978-1-4614-1629-6.pdf
\\\begin{itemize}
\item 2011
\item … (collection)
\end{itemize}
\item Trajectory Data Mining: An Overview \cite{Zheng_2015} %TrajectoryDataMining-tist-yuzheng.pdf
\\\begin{itemize}
\item 2015
\item systematic survey on the major research into trajectory data mining
\item methods that transform trajectories into other data formats, such as graphs, matrices, and tensors
\end{itemize}
\item Efficient Motif Discovery in Spatial Trajectories Using Discrete Fréchet Distance \cite{tang_motif} %/home/clemens/Downloads/Efficient motif discovery in spatial trajectories using discrete.pdf
\\\begin{itemize}
\item 2017
\item The discrete Fréchet distance (DFD) captures perceptual and geographical similarity between discrete trajectories
\end{itemize}
\item Mining individual behavior pattern based on significant locations and spatial trajectories \cite{Chen_2012} %TODO
\\\begin{itemize}
\item 2012
\item BP-Mine framework, which consists of three phases, that is, location extraction, trajectory modeling and behavior pattern mining
\item Raw WiFi RSS readings and accelerometer sensor data
\end{itemize}
\item Machine Learning and Data Mining in Pattern Recognition \cite{pattern_recog} %/home/clemens/Downloads/978-3-642-23199-5.pdf
\\\begin{itemize}
\item 2011
\item … (conference collection)
\end{itemize}
\end{itemize}
\section{log processing}
\begin{itemize}
\item Analyse von Logs mit Open-Source-Werkzeugen \cite{steinegger2016analyse}
\begin{itemize}
\item 2016
\end{itemize}
\item Monitoring mixed-language applications with elastic search, logstash and kibana (elk) \cite{andreassen2015monitoring} %/home/clemens/Downloads/wepgf041.pdf
\begin{itemize}
\item 2015
\item show how we can process almost any type of structured or unstructured data source
\end{itemize}
\item Aggregated containerized logging solution with fluentd, elasticsearch and kibana \cite{yang2016aggregated}
\begin{itemize}
\item 2016
\end{itemize}
\item Analysis of logs by using logstash \cite{sanjappa2017analysis}
\begin{itemize}
\item 2017
\end{itemize}
\item Performance monitoring and optimization \cite{matotek2017performance}
\begin{itemize}
\item 2017
\end{itemize}
\item A practical guide to monitoring and alerting with time series at scale \cite{wilkinson2017practical}
\begin{itemize}
\item 2017
\end{itemize}
\end{itemize}
\section{toDO(i)}
\begin{itemize}
\item Using High-Resolution {GPS} Tracking Data of Bird Flight for Meteorological Observations \cite{Treep_2016}
\begin{itemize}
\item 2016
\item …
\end{itemize}
\item Combining {GPS} {\&} survey data improves understanding of visitor behaviour \cite{East_2017}
\begin{itemize}
\item 2017
\item …
\end{itemize}
\item A general framework for collecting and analysing the tracking data of cruise passengers at the destination \cite{Ferrante_2016}
\begin{itemize}
\item 2016
\item …
\end{itemize}
\item Moves app: a digital diary to track physical activity and location \cite{Evenson_2016}
\begin{itemize}
\item 2016
\item …
\end{itemize}
\item Automated Urban Travel Interpretation: A Bottom-up Approach for Trajectory Segmentation \cite{Das_2016}
\begin{itemize}
\item 2016
\item …
\end{itemize}
\item Identification of activity stop locations in {GPS} trajectories by density-based clustering method combined with support vector machines \cite{Gong_2015} %/home/clemens/Downloads/s40534-015-0079-x.pdf
\begin{itemize}
\item 2015
\item …
\end{itemize}
\item Deriving Personal Trip Data from {GPS} Data: A Literature Review on the Existing Methodologies \cite{Gong_2014} %/home/clemens/Downloads/1-s2.0-S1877042814041597-main.pdf
\begin{itemize}
\item 2014
\item …
\item recommends: comparison of travel diaries; extracting activity travel diaries from gps data
\end{itemize}
\item Post-processing Procedures for Passive {GPS} based Travel Survey \cite{Liu_2013} % /home/clemens/Downloads/1-s2.0-S1877042813021642-main.pdf
\begin{itemize}
\item 2013
\item restore the sequences of data points, both in space and time: trips and activities occurred in the survey time should be identifiable chronologically
\end{itemize}
\item Everyday Cycling in Urban Environments: Understanding Behaviors and Constraints in Space-Time \cite{Yeboah_2014} % /home/clemens/Downloads/Everyday_cycling_in_urban_environments_U.pdf
\begin{itemize}
\item 2014
\item enhance data availability to understand cycling behaviors
\item data was used together with the area cycling infrastructure data
\item Computational Approaches for Urban Environments /home/clemens/Downloads/978-3-319-11469-9.pdf
\end{itemize}
\item A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Database \cite{Ke_2014}
\begin{itemize}
\item 2014
\item …
\end{itemize}
\item Why {GPS} makes distances bigger than they are \cite{Ranacher_2015} %/home/clemens/Downloads/Why GPS makes distances bigger than they are.pdf
\begin{itemize}
\item 2015
\item measurement error causes a systematic bias in distances recorded with a GPS
\item This error cancels out when average speed, distance or direction is calculated along the trajectory
\end{itemize}
\item Transportation mode-based segmentation and classification of movement trajectories \cite{Biljecki_2013} %/home/clemens/Downloads/Transportation mode based segmentation and classification of movement trajectories.pdf
\begin{itemize}
\item 2013
\item method for segmenting movement data into single-mode segments and for classifying them according to the transportation mode used
\end{itemize}
\item Children in schoolyards: Tracking movement patterns and physical activity in schoolyards using global positioning system and heart rate monitoring \cite{Fj_rtoft_2009} %/home/clemens/Downloads/Fjortoftetal.2009.pdf
\begin{itemize}
\item 2009
\item h ow the yard invited physical activity
\item The methods of GPS tracking and HR monitoring seemed to be applicable for this purpose. For further studies with young children the equipment needs adjustments.
\item => gps in small scale places
\end{itemize}
\item Places as intersecting flows: Mapping urban morphologies, functional constellations and pedestrian rhythms \cite{pafka2013places} %/home/clemens/Downloads/Places_as_Intersecting_Flows_Mapping_Urb.pdf
\begin{itemize}
\item 2013
\item no gps, social concepts
\end{itemize}
\item GPS, GIS and personal travel surveys: an exercise in visualisation \cite{stopher2002gps} %/home/clemens/Downloads/2002_Stopher_Bullock_Jiang.pdf
\begin{itemize}
\item 2002
\item produce maps and other visual representations of the travel
\end{itemize}
\item Analyzing pedestrian movement in mataf using gps and gis to support space redesign \cite{koshak2008analyzing}
\begin{itemize}
\item 2008
\item …
\end{itemize}
\end{itemize}

294
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\begin{comment}
\begin{itemize}
\item Sensing Human Activity: GPS Tracking \cite{van_der_Spek_2009} %/home/agp8x/Downloads/sensors-09-03033.pdf
\\\begin{itemize}
\item 2009
\item evaluate the sensory qualities that GPS technology offers for researching measures of urban quality
\item value of GPS as a sensor technology in urban studies
\end{itemize}
\item Exploring Patterns of Movement Suspension in Pedestrian Mobility \cite{GEAN:GEAN818} %/home/agp8x/Downloads/Exploring_patterns_of_movement_suspension_in_pedestrian_mobility.pdf
\\\begin{itemize}
\item 2011
\item explore patterns of movement suspension
\item classify the movement vectors and identify the location of movement suspension patterns
\item The results have shown that most of the detected patterns formed spatial clusters located around places that represent some attraction in the context in which the pedestrians were moving. For the players in the urban mobile game, suspension patterns were located at the checkpoints and a-priori known locations of gaming events.
\end{itemize}
\item Using location-based tracking data to analyze the movements of city tourists \cite{Modsching:2008:1098-3058:31} %/home/agp8x/Downloads/fulltext.pdf
\\\begin{itemize}
\item 2008
\item spatial distribution of visitors in various areas is visualized and analyzed
\item distribution of walking speeds, areas of slowdown are identified and subsequently clustered into activity areas
\end{itemize}
\item GPS in Pedestrian and Spatial Behaviour Surveys \cite{nielsen2004gps} %/home/agp8x/Downloads/TSN_HHH_Cities_for_people_2004.pdf
\\\begin{itemize}
\item 2004
\item possibilities in spatial behaviour and pedestrian-surveys with GPS and electronic questionnaires
\end{itemize}
\item Monitoring pedestrian spatio-temporal behaviour \cite{millonig2007monitoring} %/home/agp8x/Downloads/paper03millonig.pdf
\\\begin{itemize}
\item 2007
\item observation and interpretation of pedestrian walking patterns and route decision behaviour
\end{itemize}
\item Analysis of Tourist Behavior Based on Tracking Data Collected by GPS \cite{tourist_analysis2012} %12.pdf
\\\begin{itemize}
\item 2012
\item a user-friendly system that integrates the tracking of visitor spatial activity, the elaboration of such data according to structured formats, and their visualization through a webbased viewer
\item design and validation of this comprehensive methodology, and the early results from a test application
\end{itemize}
\item Spatial Game Analytics \cite{Drachen2013} %/home/agp8x/ownCloud/uni/ma/Neuer Ordner/978-1-4471-4769-5_17.pdf
\\\begin{itemize}
\item 2013
\item Introduction to spatial game analytics and the current state-of-the-art in games development and games research.
\item Advice and ideas on how to get started with spatial analysis of behavioral game telemetry data
\item recommendations:
\begin{verbatim}
Dankoff, J. (2011, September 12). Game telemetry with playtest DNA on assassin’s creed. The engine room. URL: http://engineroom.ubi.com/game-telemetry-with-playtest-dna-on-assassins-creed/
Demers, N. (2008). Fundamentals of geographical information systems. URL: http://www.amazon.com/Fundamentals-Geographical-Information-Systems-Michael/dp/0470129069
Drachen, A., & Canossa, A. (2011). Evaluating motion: Spatial user behaviour in virtual environments. URL: http://andersdrachen.files.wordpress.com/2011/01/05_drachen_ijart.pdf
Hoobler, N., Humphreys, G., & Agrawala, M. (2004). Visualizing competitive behaviors in multiuser virtual environments . URL: http://www.cs.virginia.edu/~gfx/pubs/lithium/
Houghton, S. (2011). Balance and fl ow maps . URL: http://altdevblogaday.com/2011/06/01/balance-and-flow-maps-2/
Kennerly, K. (2003). Better game design through data mining . URL: http://www.gamasutra.com/view/feature/2816/better_game_design_through_data_.php
Pruett, C. (2010). Hot failure: Tuning gameplay with simple player metrics. URL: http://www.gamasutra.com/view/feature/6155/hot_failure_tuning_gameplay_with_.php?print=1
Thompson, C. (2007). Halo 3: How Microsoft labs invented a new science of play. URL: http://www.wired.com/gaming/virtualworlds/magazine/15-09/ff_halo
Zoeller, G. (2011). MMO rapid content iteration. URL: http://gdc.gulbsoft.org/
\end{verbatim}
same book:
\begin{verbatim}
18 Visual Game Analytics ............................................................................ 403
Ben Medler
19 Visual Analytics Tools – A Lens into Player’s
Temporal Progression and Behavior ..................................................... 435
Magy Seif El-Nasr, André Gagné, Dinara Moura, and Bardia Aghabeigi
\end{verbatim}
\item -----
\item beloved, but basic: heatmap
\item spatial contexts in computer games, adapting GIS technology
\end{itemize}
\item GeoGame analytics – A cyber-enabled petri dish for geographic modeling and simulation \cite{AHLQVIST20181} %/home/agp8x/ownCloud/uni/ma/Neuer Ordner/1-s2.0-S0198971517304234-main.pdf
\\\begin{itemize}
\item 2018
\item we present the emerging area of Spatial Game Analytics that provides an uncharted area for data-intensive geospatial scenario analysis
\item Exploratory GeoGame analytics to mine spatial behavior of players, identify how variations in the rules and varying locations affect the simulation outcomes.
\item -----
\item manual process: \textit{In our subsequent analysis we first converted our game database to
ArcMap data in order to link the parcel map with game play attributes.
We then used ArcMap primarily as a visual exploration tool to display
parcels and related data, like yield, parcel use, etc., on those parcels.
We also performed some exploratory data analysis in SPSS where our
focus was to look for correlations between variables in the game.}
\end{itemize}
\end{itemize}
\section{Metriken sammeln}
\begin{itemize}
\item Geogames: Designing Location-Based Games from Classic Board Games\cite{1705427}
\\\begin{itemize}
\item …
\end{itemize}
\item … \cite{heinz2015agent}
\\\begin{itemize}
\item …
\end{itemize}
\item … \cite{schlieder2005geogames}
\\\begin{itemize}
\item …
\end{itemize}
\item locomotion-action %TODO citation Reducing Location Overhead in Educational Geogames
\\\begin{itemize}
\item …
\end{itemize}
\end{itemize}
\section{umgang mit trajektorien}
\begin{itemize}
\item Computing with Spatial Trajectories \cite{zheng2011computing} %10.1007%2F978-1-4614-1629-6.pdf
\\\begin{itemize}
\item 2011
\item … (collection)
\end{itemize}
\item Trajectory Data Mining: An Overview \cite{Zheng_2015} %TrajectoryDataMining-tist-yuzheng.pdf
\\\begin{itemize}
\item 2015
\item systematic survey on the major research into trajectory data mining
\item methods that transform trajectories into other data formats, such as graphs, matrices, and tensors
\end{itemize}
\item Efficient Motif Discovery in Spatial Trajectories Using Discrete Fréchet Distance \cite{tang_motif} %/home/clemens/Downloads/Efficient motif discovery in spatial trajectories using discrete.pdf
\\\begin{itemize}
\item 2017
\item The discrete Fréchet distance (DFD) captures perceptual and geographical similarity between discrete trajectories
\end{itemize}
\item Mining individual behavior pattern based on significant locations and spatial trajectories \cite{Chen_2012} %TODO
\\\begin{itemize}
\item 2012
\item BP-Mine framework, which consists of three phases, that is, location extraction, trajectory modeling and behavior pattern mining
\item Raw WiFi RSS readings and accelerometer sensor data
\end{itemize}
\item Machine Learning and Data Mining in Pattern Recognition \cite{pattern_recog} %/home/clemens/Downloads/978-3-642-23199-5.pdf
\\\begin{itemize}
\item 2011
\item … (conference collection)
\end{itemize}
\end{itemize}
\section{log processing}
\begin{itemize}
\item Analyse von Logs mit Open-Source-Werkzeugen \cite{steinegger2016analyse}
\begin{itemize}
\item 2016
\end{itemize}
\item Monitoring mixed-language applications with elastic search, logstash and kibana (elk) \cite{andreassen2015monitoring} %/home/clemens/Downloads/wepgf041.pdf
\begin{itemize}
\item 2015
\item show how we can process almost any type of structured or unstructured data source
\end{itemize}
\item Aggregated containerized logging solution with fluentd, elasticsearch and kibana \cite{yang2016aggregated}
\begin{itemize}
\item 2016
\end{itemize}
\item Analysis of logs by using logstash \cite{sanjappa2017analysis}
\begin{itemize}
\item 2017
\end{itemize}
\item Performance monitoring and optimization \cite{matotek2017performance}
\begin{itemize}
\item 2017
\end{itemize}
\item A practical guide to monitoring and alerting with time series at scale \cite{wilkinson2017practical}
\begin{itemize}
\item 2017
\end{itemize}
\end{itemize}
\section{toDO(i)}
\begin{itemize}
\item Using High-Resolution {GPS} Tracking Data of Bird Flight for Meteorological Observations \cite{Treep_2016}
\begin{itemize}
\item 2016
\item …
\end{itemize}
\item Combining {GPS} {\&} survey data improves understanding of visitor behaviour \cite{East_2017}
\begin{itemize}
\item 2017
\item …
\end{itemize}
\item A general framework for collecting and analysing the tracking data of cruise passengers at the destination \cite{Ferrante_2016}
\begin{itemize}
\item 2016
\item …
\end{itemize}
\item Moves app: a digital diary to track physical activity and location \cite{Evenson_2016}
\begin{itemize}
\item 2016
\item …
\end{itemize}
\item Automated Urban Travel Interpretation: A Bottom-up Approach for Trajectory Segmentation \cite{Das_2016}
\begin{itemize}
\item 2016
\item …
\end{itemize}
\item Identification of activity stop locations in {GPS} trajectories by density-based clustering method combined with support vector machines \cite{Gong_2015} %/home/clemens/Downloads/s40534-015-0079-x.pdf
\begin{itemize}
\item 2015
\item …
\end{itemize}
\item Deriving Personal Trip Data from {GPS} Data: A Literature Review on the Existing Methodologies \cite{Gong_2014} %/home/clemens/Downloads/1-s2.0-S1877042814041597-main.pdf
\begin{itemize}
\item 2014
\item …
\item recommends: comparison of travel diaries; extracting activity travel diaries from gps data
\end{itemize}
\item Post-processing Procedures for Passive {GPS} based Travel Survey \cite{Liu_2013} % /home/clemens/Downloads/1-s2.0-S1877042813021642-main.pdf
\begin{itemize}
\item 2013
\item restore the sequences of data points, both in space and time: trips and activities occurred in the survey time should be identifiable chronologically
\end{itemize}
\item Everyday Cycling in Urban Environments: Understanding Behaviors and Constraints in Space-Time \cite{Yeboah_2014} % /home/clemens/Downloads/Everyday_cycling_in_urban_environments_U.pdf
\begin{itemize}
\item 2014
\item enhance data availability to understand cycling behaviors
\item data was used together with the area cycling infrastructure data
\item Computational Approaches for Urban Environments /home/clemens/Downloads/978-3-319-11469-9.pdf
\end{itemize}
\item A Hybrid Spatio-Temporal Data Indexing Method for Trajectory Database \cite{Ke_2014}
\begin{itemize}
\item 2014
\item …
\end{itemize}
\item Why {GPS} makes distances bigger than they are \cite{Ranacher_2015} %/home/clemens/Downloads/Why GPS makes distances bigger than they are.pdf
\begin{itemize}
\item 2015
\item measurement error causes a systematic bias in distances recorded with a GPS
\item This error cancels out when average speed, distance or direction is calculated along the trajectory
\end{itemize}
\item Transportation mode-based segmentation and classification of movement trajectories \cite{Biljecki_2013} %/home/clemens/Downloads/Transportation mode based segmentation and classification of movement trajectories.pdf
\begin{itemize}
\item 2013
\item method for segmenting movement data into single-mode segments and for classifying them according to the transportation mode used
\end{itemize}
\item Children in schoolyards: Tracking movement patterns and physical activity in schoolyards using global positioning system and heart rate monitoring \cite{Fj_rtoft_2009} %/home/clemens/Downloads/Fjortoftetal.2009.pdf
\begin{itemize}
\item 2009
\item h ow the yard invited physical activity
\item The methods of GPS tracking and HR monitoring seemed to be applicable for this purpose. For further studies with young children the equipment needs adjustments.
\item => gps in small scale places
\end{itemize}
\item Places as intersecting flows: Mapping urban morphologies, functional constellations and pedestrian rhythms \cite{pafka2013places} %/home/clemens/Downloads/Places_as_Intersecting_Flows_Mapping_Urb.pdf
\begin{itemize}
\item 2013
\item no gps, social concepts
\end{itemize}
\item GPS, GIS and personal travel surveys: an exercise in visualisation \cite{stopher2002gps} %/home/clemens/Downloads/2002_Stopher_Bullock_Jiang.pdf
\begin{itemize}
\item 2002
\item produce maps and other visual representations of the travel
\end{itemize}
\item Analyzing pedestrian movement in mataf using gps and gis to support space redesign \cite{koshak2008analyzing}
\begin{itemize}
\item 2008
\item …
\end{itemize}
\end{itemize}
\end{comment}

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\section{State of research}
%TODO
\subsection{Log processing}
System administrators and developers face a daily surge of log files from applications, systems, and servers.
For knowledge extraction, a wide range of tools is in constant development for such environments.
Currently, an architectural approach with three main components is most frequently applied.
This components are divided into aggregation \& creation, storage, and analysis \& frontend.
A popular example is the ELK stack consisting of Elastic Search, Logstash, and Kibana \cite{andreassen2015monitoring,yang2016aggregated,steinegger2016analyse,sanjappa2017analysis}.
In \autoref{tab:logs} some implementations of these components are listed according to the main focus.
For this list, cloud-based services were not taken into account.
A clear classification is not always possible, as some modules integrate virtually all features necessary, as is the case with the Graphite tool set.
\begin{longtable}[H]{cp{0.2\textwidth}p{0.2\textwidth}}
Collection & Database & Frontend\\
\hline
Logstash\footnote{\url{https://www.elastic.co/de/products/logstash}} & Elatisc Search\footnote{\url{https://www.elastic.co/de/products/elasticsearch}} & Kibana\footnote{\url{https://www.elastic.co/de/products/kibana}}\\
Collectd\footnote{\url{https://collectd.org/}} & Influx DB\footnote{\url{https://www.influxdata.com/}} & Grafana\footnote{\url{https://grafana.com}}\\
Icinga\footnote{\url{https://www.icinga.com/products/icinga-2/}} & Whisper\footnote{\url{https://github.com/graphite-project/whisper}} & Graphite\footnote{\url{https://graphiteapp.org/}}\\
StatsD\footnote{\url{https://github.com/etsy/statsd}} & Prometheus\footnote{\url{https://prometheus.io/}} & \\
%\footnote{\url{}} & \footnote{\url{}} & \footnote{\url{}}\\
\caption{Log processing components}
\label{tab:logs}
\end{longtable}
\subsubsection{Collection}
Nearly all services designed for log collection offer multiple interfaces for submitting log data.
By way of illustration, Logstash features a long list of input plugins from streaming files over an HTTP API to proprietary vendor sources like Amazon Web Services (AWS)\footnote{\url{https://www.elastic.co/guide/en/logstash/current/input-plugins.html}}. \nomenclature{\m{A}mazon \m{W}eb \m{S}ervices}{AWS}
Aside from aggreation, the topic of log creation is covered from host-based monitoring solutions like Icinga to application centric approaches with e.g. StatsD embedded in the application source code.
\subsubsection{Databases}
The key component for a log processing system is the storage.
While relational database management systems (RDBMS) \nomenclature{\m{R}elational \m{D}ata\m{b}ase \m{M}anagement \m{S}ystem}{RDBMS} can be suitable for small-scale solutions, the temporal order of events impose many pitfalls.
For instance, django-monit-collector\footnote{\url{https://github.com/nleng/django-monit-collector}} as open alternative to the proprietary MMonit cloud service\footnote{\url{https://mmonit.com/monit/\#mmonit}} assures temporal coherence through lists of timestamps and measurement values stored as JSON strings in a RDBMS. \nomenclature{\m{J}ava\m{s}cript \m{O}bject \m{N}otation}{JSON}
This strategy forces the RDBMS and the application to deal with growing amounts of data, as no temporal selection can be performed by the RDBMS itself.
During the evaluation in \cite{grossmann2017monitoring}, this phenomena rendered the browser-based visualization basically useless and impeded the access with statistical tools significantly.
Time Series Databases (TSDB) are specialized on chronological events.
%TODO
%TODO RRD
With a focus on chronological events, Time Series Databases (TSDB) are commonly used in these scenarios. \nomenclature{\m{T}ime \m{S}eries \m{D}ata\m{b}ase}{TSDB}
\subsubsection{Frontend}
Frontends utilize the powerful query languages of the TSDB systems backing them.
Grafana e.g. provides customizable dashboards with graphing and mapping support \cite{komarek2017metric}.
Additional functionality can be added with plugins.
%TODO
%TODO: weather station screenshot
%%%
\begin{itemize}
\item ELK (Elastic search, Logstash, Kibana)\cite{andreassen2015monitoring} \cite{yang2016aggregated} \cite{steinegger2016analyse} \cite{sanjappa2017analysis}
\item Collectd, Influx DB, Grafana \cite{komarek2017metric}
\item …
\end{itemize}
\begin{itemize}
\item[+] widely deployed
\item[+] powerful query languages %TODO example
\item mainly web/container/hardware monitoring
\item[-] spatial analysis: heavily anonymized
\item[-] fast-paced environment
\end{itemize}
\subsection{Pedestrian traces}
Analyzing pedestrian movement … based on GPS logs
\subsubsection{Data basis: GPS}
\subsubsection{Activity Mining}
\subsubsection{Visualization}
\begin{itemize}
\item GPS overestimates systematically \cite{Ranacher_2015}
\item GPS is a suitable instrument for spatio-temporal data\cite{van_der_Spek_2009}
\item Activity mining \cite{Gong_2014}
\begin{itemize}
\item Speed-based Clustering \cite{ren2015mining}
%\item \cite{Ferrante_2016} % closed access
\item Machine Learning \cite{pattern_recog} %TODO
\end{itemize}
\item E.g.: Improve tourist management \cite{tourist_analysis2012}
\end{itemize}
\image{.81\textwidth}{../../PresTeX/images/strava}{Heatmap: Fitnesstracker\cite{strava}}{img:strava}
\image{.72\textwidth}{../../PresTeX/images/space-time}{Space-time cube examples\cite{bach2014review}}{img:spacetime}
\image{\textwidth}{../../PresTeX/images/traj-pattern}{Flock and meet trajectory pattern\cite{jeung2011trajectory}}{img:traj-pattern}
\image{\textwidth}{../../PresTeX/images/generalization}{Trajectories and generalizations with varying radius parameter \cite{adrienko2011spatial}}{img:generalization}
\subsection{Analyzing games}
\begin{itemize}
\item there's more than heatmaps
\item combine position with game actions
\item identify patterns, balancing issues
\item manual processes %\citetitle{Drachen2013}\citetitle{AHLQVIST20181}
\end{itemize}
%\image{.5\textwidth}{game-an}{chat logs with players location \cite{Drachen2013}}{img:chatlogs}
%\image{.5\textwidth}{ac3-death}{identify critical sections \cite{Drachen2013}}{img:ac3death}
\twofigures{0.5}{../../PresTeX/images/game-an}{Chat logs with players location}{img:chatlogs}{../../PresTeX/images/ac3-death}{Identify critical sections}{img:ac3death}{Game analytics \cite{Drachen2013}}{fig:gameanal}
\subsection{Summary}
\begin{itemize}
\item Log processing: Powerful stacks
\item Movement analysis: Large field already explored (GPS influence, Patterns, Behavior recognition, …)
\item Track rendering: Track (with attributes), Space-time cube, Heatmap, …
\item Spatial analysis of digital games with GIS
\item Analysis of location based games: Laborious manual process
\end{itemize}

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@ -1,57 +1,67 @@
\section{modulare auswahl von metriken}
\section{Requirements}
Wait, what did I want to do again?
\begin{itemize} \begin{itemize}
\item base set of analytic functions \item Per-game importer (Web client, File loader, …)
\item extendable for special cases \item Analyzer modules (number crunching)
\item … \item Output \& Visualization (CSV, [Geo]JSON, KML, Graphs, …)
\item Interface (Configuration)
\begin{itemize}
\item Expert users/researchers
\item Staging/designing staff
\end{itemize}
\item Cross-game comparisons
\item Integration of external data (questionnaire results)
\end{itemize} \end{itemize}
\section{spiel unabhängig} \section{Evaluating Kibana}
\begin{itemize}
\item log importer/transformer necessary To evaluate whether Kibana is a viable approach for the given requirements, I have created a test environment.
\end{itemize} This setup is documented in \autoref{app:kibana}.
Two sample datasets were loaded into the Elasticsearch container through HTTP POST requests: \texttt{curl -H 'Content-Type: application/x-ndjson' -XPOST 'elastic:9200/\_bulk?pretty' --data-binary @gamelog.json}.
Once Kibana was told which fields hold the spatial information, it is possible to have a first visualization.
However, this view is optimized for the context of web log processing, so it has a rather low spatial resolution as shown in \autoref{img:kibana} and \autoref{img:kibana2}.
\section{log processing taugt nich} … taugt nich
\begin{itemize} \begin{itemize}
\item powerful timeseries database \item powerful timeseries database
\item complex setup \item complex setup
\item fast paced environment \item fast paced environment
\item low spatial resolution => privacy optimized \item low spatial resolution => privacy optimized
\end{itemize} \end{itemize}
\image{\textwidth}{../../PresTeX/images/kibana}{Game trace in Kibana}{img:kibana}
\image{\textwidth}{../../PresTeX/images/kibana2}{Game trace in Kibana}{img:kibana2}
\section{architekturmodell} \section{Architecture}
Game independance:
\begin{itemize} \begin{itemize}
\item map-reduce ähnlich \item log importer/transformer necessary
\begin{enumerate} \end{itemize}
\item input transformation \begin{itemize}
\item analysis/extension (looping) \item Based on map-reduce
\item render (image,csv,json,…) \item Map: Analysis
\end{enumerate} \begin{itemize}
\image{.75\textwidth}{flowchart}{arch dlowcahrt}{img:flowchart} \item Iterate Log entries
\item Feed log entry through analyzer queue
\begin{itemize}
\item Augment entries
\item Filter entries
\item Sequential order
\end{itemize}
\end{itemize}
\item Reduce: Collect summaries from analyzers
\begin{itemize}
\item Rendering
\item Post-processing, Comparison, …
\end{itemize}
\item standalone (indep. of any game) \item standalone (indep. of any game)
\item own client for game server (due to CORS/XSS prevention prohibiting shared use of game server assets in other host) \item own client for game server (due to CORS/XSS prevention prohibiting shared use of game server assets in other host)
\item API for integration \item API for integration
\item allow load distribution \item allow load distribution
\end{itemize} \end{itemize}
\image{\textwidth}{map-reduce.pdf}{Data flows}{img:flow}
==== \image{\textwidth}{architecture.pdf}{archoitecure overview}{img:arch}
\begin{itemize}
\item map-reduce like
\item map: analyse-läufe
\begin{itemize}
\item log-eintrags basiert
\item reichern log an
\item filtern log
\item metriken über komplette log => extern gespeichert
\item => evtl. Abhängigkeiten in Reihenfolge
\item basis-funktionalität || spiel-bezogen
\end{itemize}
\item reduce: Operieren mit Ergebnissen (über alle verarbeiteten logs hinweg)
\begin{itemize}
\item render
\item verteilung, …
\end{itemize}
\end{itemize}

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\section{klassenstruktur}
\section{konfigurierbarkeit (log import)}
\section{...}
\section{1. vortrag}
\section{Outlook: Implementation}
\subsection{Implementation}
Analysis
\begin{itemize}
\item Python (3.6)
\item Standalone library/CLI tool
\item Web based configuration/Runner/API (Flask)
\end{itemize}
Rendering
\begin{itemize}
\item Matplotlib, Numpy
\begin{itemize}
\item Graphs
\end{itemize}
\item Javascript
\begin{itemize}
\item Leaflet
\item Web visualization: Maps, Tracks, …
\end{itemize}
\end{itemize}
\pic{.5\textwidth}{../../PresTeX/images/matplotlib}
\pic{.5\textwidth}{../../PresTeX/images/python}
\pic{.4\textwidth}{../../PresTeX/images/flask}
\pic{.4\textwidth}{../../PresTeX/images/leaflet}
\subsection{Examples}
Configuration \& results
\twofigures{0.5}{../../PresTeX/images/oeb-kml}{Analyzer configuration}{img:oebkml}{../../PresTeX/images/oeb-ge}{Result visualized}{img:oebge}{Example: Generate KML tracks (BioDiv2Go; Oberelsbach2016)}{fig:oeb2016}
{ActivityMapper}
\image{.7\textwidth}{../../PresTeX/images/track-fi}{Combined screen activity and spatial progress}{img:trackfi}
{Graphs}
\image{\textwidth}{../../PresTeX/images/simu-retries}{Experimentational rounds}{img:retries}
\image{\textwidth}{../../PresTeX/images/speed}{Speed distribution}{img:speed}
\image{.9\textwidth}{../../PresTeX/images/time-rel}{Time distribution}{img:time}

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\section{Setup für Kibana} \label{app:kibana}
\lstinputlisting[language=yaml,caption={Docker-compose file for Kibana test setup},label=code:kibana]{code/kibana-docker-compose.yml}
\section{Biogames Server Dockerized}
\image{\textwidth}{biogames.pdf}{Dockerized setup for biogames}{img:bd2gdocker}
\lstinputlisting[language=yaml,caption={Docker-compose file for Biogames server},label=code:bd2s]{code/biogames/docker-compose.yml}
\lstinputlisting[language=yaml,caption={Dockerfile for Biogames server},label=code:bd2d]{code/biogames/Dockerfile}
\lstinputlisting[language=bash,caption={Entrypoint for Biogames docker container},label=code:bd2e]{code/biogames/start.sh}
\lstinputlisting[language=yaml,caption={Dockerfile for Traefik reverse proxy},label=code:bd2t]{code/traefik.yml}

9
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@ -487,3 +487,12 @@ keywords = "Games, Agent based models, Simulations, Analytics"
year={2011}, year={2011},
publisher={Springer} publisher={Springer}
} }
@inproceedings{grossmann2017monitoring,
title={Monitoring Container Services at the Network Edge},
author={Gro{\ss}mann, Marcel and Klug, Clemens},
booktitle={Teletraffic Congress (ITC 29), 2017 29th International},
volume={1},
pages={130--133},
year={2017},
organization={IEEE}
}

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@ -11,18 +11,23 @@
\input{content/3-solution} \input{content/3-solution}
\chapter{umsetzung} \chapter{umsetzung}
%\input{content/4-implementation} \input{content/4-implementation}
\section{klassenstruktur}
\section{konfigurierbarkeit (log import)}
\section{...}
\chapter{evaluierung} \chapter{evaluierung}
%\input{content/5-evaluation} %\input{content/5-evaluation}
\section{wie viele metriken umsetzbar?} \section{Portierbarkeit (anderes game)}
\section{erweiterbarkeit}
\begin{verbatim}
…
\end{verbatim}
\chapter{diskussion} \chapter{diskussion}
%\input{content/6-discussion} %\input{content/6-discussion}
\begin{verbatim}
…
\end{verbatim}
\section{dummies} \section{dummies}
\cite{zheng2011computing} \cite{zheng2011computing}

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graph{
//rankdir="LR";
{
rank=same;
s [label="Web-App\nFlask"];
c [label="Worker\nCelery"];
}
db [label="DB\nRedis"];
t [label="HTTP frontend\nTraefik"];
n [label="Static file server\nNginx"];
/*{
rank=same;
b [label="Biogames"];
g [label="Geogames server"];
};*/
s -- db [label="Internal network"];
c -- db [];
t -- s [label=" External network,\nlabel"];
t -- n [];
c -- n [label="Shared volume "];
/*s -- b [style="dotted"];
c -- b [style="dotted"];
s -- g [style="dotted"];
c -- g [style="dotted"];*/
}

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@ -0,0 +1,11 @@
graph{
//rankdir="LR";
s [label="Web-App\nBiogames"];
c [label="Worker\nCelery"];
db [label="DB\nPostgres"];
t [label="HTTP frontend\nTraefik"];
s -- db [];
c -- db [label="Internal network"];
t -- s [label=" External network,\nlabel"];
}

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@ -33,6 +33,7 @@
%=============================================================================== %===============================================================================
\newcommand\meta{../meta} \newcommand\meta{../meta}
\input{\meta/config/commands} \input{\meta/config/commands}
\DeclareOldFontCommand{\bf}{\normalfont\bfseries}{\mathbf}
%=============================================================================== %===============================================================================
% LATEX-Dokument % LATEX-Dokument
%=============================================================================== %===============================================================================
@ -46,7 +47,7 @@
% Zur Auswahl der Sprache im folgenden Befehl % Zur Auswahl der Sprache im folgenden Befehl
% ngerman für deutsch eintragen, english für Englisch. % ngerman für deutsch eintragen, english für Englisch.
%=============================================================================== %===============================================================================
\selectlanguage{ngerman} \selectlanguage{english}
\setstretch{1.1} \setstretch{1.1}
%% Titelseite %% Titelseite
\maketitle \maketitle
@ -61,7 +62,8 @@
\newpage \newpage
\listoftables \listoftables
\newpage \newpage
\listofalgorithms %\listofalgorithms
\lstlistoflistings
\newpage \newpage
%% Falls nur Abkuerzungs- oder Symbolverzeichnis benoetigt wird, folgende Befehle benutzen %% %% Falls nur Abkuerzungs- oder Symbolverzeichnis benoetigt wird, folgende Befehle benutzen %%
%\printnomenclature %\printnomenclature
@ -108,6 +110,7 @@
\appendix \appendix
\setstretch{1.5} \setstretch{1.5}
\chapter{Anhang} \chapter{Anhang}
\input{content/appendix}
\setstretch{1.1} \setstretch{1.1}
\cleardoubleemptypage \cleardoubleemptypage

2
meta/config/commands.tex
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@ -277,7 +277,7 @@ Ich erkläre hiermit gemäß § 17 Abs. 2 APO, dass ich die vorstehende \degree
%=============================================================================== %===============================================================================
% Listing Styles % Listing Styles
%=============================================================================== %===============================================================================
\lstset{basicstyle=\ttfamily,showstringspaces=false,commentstyle=\color{unibagrayI},keywordstyle=\color{unibablueI},breaklines=true} \lstset{basicstyle=\ttfamily,showstringspaces=false,commentstyle=\color{unibagrayI},keywordstyle=\color{unibablueI},breaklines=true,captionpos=b}
\DeclareFixedFont{\ttb}{T1}{txtt}{bx}{n}{9} % for bold \DeclareFixedFont{\ttb}{T1}{txtt}{bx}{n}{9} % for bold
\DeclareFixedFont{\ttm}{T1}{txtt}{m}{n}{9} % for normal \DeclareFixedFont{\ttm}{T1}{txtt}{m}{n}{9} % for normal
\lstset{ \lstset{