With an administrative background, the first approach to log processing which comes to mind are the various log processing frameworks.
\autoref{sec:logproctheo} shows the current state of tools and processes for managing large volumes of log and time series data.
An overview of the field of pedestrian track analysis is located in \autoref{sec:pedest}.
Finally, in \autoref{sec:gametheo} the connection of spatial analysis and digital game optimizations is showcased.

\section{Log processing}\label{sec:logproctheo}
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}. \nomenclature{\m{E}lasticSearch, \m{L}ogstash, and \m{K}ibana}{ELK}
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\furl{https://www.elastic.co/de/products/logstash} & Elastic Search\furl{https://www.elastic.co/de/products/elasticsearch} & Kibana\furl{https://www.elastic.co/de/products/kibana}\\
Collectd\furl{https://collectd.org/} & Influx DB\furl{https://www.influxdata.com/} & Grafana\furl{https://grafana.com}\\
Icinga\furl{https://www.icinga.com/products/icinga-2/} & Whisper\furl{https://github.com/graphite-project/whisper} & Graphite\furl{https://graphiteapp.org/}\\
StatsD\furl{https://github.com/etsy/statsd} & Prometheus\furl{https://prometheus.io/} & \\
%\furl{} & \furl{} & \furl{}\\

\caption{Log processing components}
\label{tab:logs}
\end{longtable}

\subsection{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)\furl{https://www.elastic.co/guide/en/logstash/current/input-plugins.html}. \nomenclature{\m{A}mazon \m{W}eb \m{S}ervices}{AWS} \nomenclature{\m{A}pplication \m{P}rogramming \m{I}nterface}{API}\nomenclature{\m{H}yper\m{t}ext \m{T}ransport \m{P}rotocol}{HTTP}

Aside from aggregation, 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\furl{https://thenewstack.io/collecting-metrics-using-statsd-a-standard-for-real-time-monitoring/}.

\subsection{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\furl{https://github.com/nleng/django-monit-collector} as open alternative to the proprietary MMonit cloud service\furl{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.
One typical use is in monitoring, e.g. server health/usage statistics, or weather stations, like the example \autoref{img:rdd} shows.
This example utilizes one of the early TSDB systems, RDDtool\furl{https://oss.oetiker.ch/rrdtool/index.en.html}.
More recently, alternatives written in modern languages are popular, like InfluxDB\furl{https://www.influxdata.com/} on Go\furl{https://golang.org/} or Whisper on Python (from the Graphite software package).
\image{\textwidth}{mgroth}{Weather station plot with RDDtool \cite{RDD}}{img:rdd}
\nomenclature{\m{T}ime \m{S}eries \m{D}ata\m{b}ase}{TSDB}

\subsection{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, e.g. for new data sources or dashboard panels with visualizations.
The query languages of the data sources is abstracted by an common user interface.


\section{Pedestrian traces}\label{sec:pedest}
Analyzing pedestrian movement based on GPS logs is an established technique.
In the following sections, \autoref{sssec:gps} provides an overview of GPS as data basis, \autoref{sssec:act} highlights some approaches to activity mining and \autoref{sssec:vis} showcases popular visualizations of tempo-spatial data.
\nomenclature{\m{G}lobal \m{P}ositioning \m{S}ystem}{GPS}

\subsection{Data basis: GPS}\label{sssec:gps}
Global navigation satellite systems (GNSS) like GPS, Galileo, GLONASS, or BeiDou are a source of positioning data for mobile users.
\nomenclature{\m{G}lobal \m{N}avigation \m{S}atellite \m{S}ystems}{GNSS}
\cite{van_der_Spek_2009} has shown that such signals provide a reliable service in many situations.
Additionally, tracks of these signals are a invaluable source of information for researching movements and movement patterns. \cite{Modsching:2008:1098-3058:31,nielsen2004gps,millonig2007monitoring}
Therefore, GNSS are suitable instruments for acquiring spatio-temporal data \cite{van_der_Spek_2009}.

However, \cite{Ranacher_2015} reminds of systematical overestimates by GPS due to interpolation errors.
To eliminate such biases of one system, \cite{Li2015} describes the combination of multiple GNSS for improved accuracy and reduced convergence time.

\subsection{Activity Mining}\label{sssec:act}
GPS (or GNSS) tracks generally only contain the raw tempo-spatial data (possibly accompanied by metadata like accuracy, visible satellites, etc.).
Any additional information needs either be logged separately or needs to be derived from the track data itself.
This activity mining allows e.g. the determination of the modes of transport used while creating the track \cite{Gong_2014}.
\cite{Gong_2015} shows the extraction of activity stop locations to identify locations where locomotion suspends for an activity in contrast to stops without activities.
Information of this kind are relevant e.g. for improvements for tourist management in popular destinations \cite{tourist_analysis2012,koshak2008analyzing,Modsching:2008:1098-3058:31}.

Beside points of interest (POIs), individual behaviour patterns can be mined from tracks, as described in \cite{ren2015mining}.
Post-processing of these patterns with machine learning enables predictions of future trajectories \cite{10.1007/978-3-642-23199-5_37}.
%TODO more??

\subsection{Visualization}\label{sssec:vis}
Visualizations help to understand data sets, especially for spatial data.

\subsubsection{Heatmap}
One of the most basic visualization of large amounts of spatial data is the heatmap.
As the example in \autoref{img:strava} shows, it allows to identify areas with high densities of data points very quickly.
This comes however with the loss of nearly all context information.
For example, the temporal information - both the time slice and the relative order of the data points - is completely absent.
A workaround is an external control element for such information to control the underlying data set.

\image{\textwidth}{../../PresTeX/images/strava}{Heatmap: Fitnesstracker \cite{strava}}{img:strava}

\subsubsection{Track attributes}
An example of a rendering methodology including more attributes, \cite{stopher2002gps} details the possibilities using cartographic signatures as seen in \autoref{img:track-attr}.
When track lines are used, there are some options to indicate attributes of the track, too.
Besides the color, e.g. the width and stroke-type of the line can indicate certain attributes.
A combination of these allows the visualization of multiple attributes at once.
However, such views are limited in the amount of tracks and attributes to display before been confusing and ambiguous.

\image{\textwidth}{track-attributes}{Track rendering with acceleration attributes \cite{stopher2002gps}}{img:track-attr}


\subsubsection{Space-time cube}
One way to address the lack of temporal context is the space-time cube concept reviewed in \cite{kraak2003space}.
By mapping an additional temporal axis as third dimension on a two-dimensional map, tracks can be rendered in a three-dimensional context.
The example in \autoref{img:spacetime} shows how such a rendering allows to identify individual movement patterns and locations of activity in between.
However, it also demonstrates the problems of the difficult interpretation of the 3D map, especially with overlapping tracks.
Beside from overcrowded ares, many people have difficulties of miss-interpreting the 3D movements.
The space flattened alternative on the right tries to reduce this problem with a spatial abstraction.

\image{\textwidth}{../../PresTeX/images/space-time}{Space-time cube examples \cite{bach2014review}}{img:spacetime}

An approach for an time-aware heatmap utilizing space-time cubes is shown in \autoref{img:spacetime2}.
This highlights hotspots of activity over an temporal axis.
\image{\textwidth}{space-time-density}{Space-time cube density examples \cite{demvsar2015analysis}}{img:spacetime2}

\subsubsection{Trajectory patterns and generalizations}
To simplify the visualization of large amounts of individual tracks, the derivation of patterns applying to the tracks allows to highlight key areas.
\autoref{img:traj-pattern} shows two examples of such patterns: Flock, where a group of tracks are aligned for some time, and meet, which defines an area of shared presence.
It is possible to apply such pattern time aware or time agnostic, i.e. whether to take the simultaneous appearance into account. \cite{jeung2011trajectory}

\image{\textwidth}{../../PresTeX/images/traj-pattern}{Flock and meet trajectory pattern \cite{jeung2011trajectory}}{img:traj-pattern}

An approach for addressing the generalization aspects necessary to visualize massive movement data is described in \cite{adrienko2011spatial}.
They work on traffic data as shown in \autoref{img:generalization}.
With an increasing generalization parameter, the flows refine to more abstract representations of travel.

\image{\textwidth}{../../PresTeX/images/generalization}{Trajectories and generalizations with varying radius parameter \cite{adrienko2011spatial}}{img:generalization}


\section{Analyzing games}\label{sec:gametheo}
Modern video games with always-on copy-protection or online master servers allow game studios to collect metrics about players' performances.
In \cite{Drachen2013}, the authors describe the use of GIS technologies for such environments.
For example, \autoref{img:chatlogs} shows a correlation between the frequency of certain keywords in the chat messages and the players' current location.
This indicates a possible bug in the game to look out for.

Not only technical problems, design errors or bad balancing can be visualized, too.
\autoref{img:ac3death} uses a heatmap to highlight areas with high failure rates during play-testing.
These failure hotspots points can then be addressed for a convenient game flow.

\image{\textwidth}{../../PresTeX/images/game-an}{Chat logs with players location \cite{Drachen2013}}{img:chatlogs}
\image{\textwidth}{../../PresTeX/images/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}

In contrast to the complete virtual games above, \cite{AHLQVIST20181} describes the mining of spatial behaviour of players through an real-world base online game.
With an focus on replicating the real world, players have to align social and natural resources.
The results of these simulations can then be used to built agent-based simulations with realistic behaviour.