wörk wörk
parent
00cf3f77fb
commit
acab120689
|
|
@ -1,4 +1,4 @@
|
|||
|
||||
\label{sec:scope}
|
||||
\subsection{Goal definition}
|
||||
A Framework for the Analysis of Spatial Game Data
|
||||
\begin{itemize}
|
||||
|
|
|
|||
|
|
@ -1,5 +1,6 @@
|
|||
\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.
|
||||
|
|
@ -31,15 +32,25 @@ Aside from aggreation, the topic of log creation is covered from host-based moni
|
|||
|
||||
\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}
|
||||
|
||||
%TODO: weather station screenshot
|
||||
|
||||
\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}
|
||||
|
|
@ -57,6 +68,10 @@ Additional functionality can be added with plugins.
|
|||
|
||||
\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}
|
||||
|
|
|
|||
|
|
@ -15,7 +15,15 @@ Wait, what did I want to do again?
|
|||
\end{itemize}
|
||||
|
||||
|
||||
\section{Experiment: Kibana}
|
||||
\section{Evaluating Kibana}
|
||||
|
||||
To evaluate whether Kibana is a viable approach for the given requirements, I have created a test environment.
|
||||
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}.
|
||||
|
||||
|
||||
… taugt nich
|
||||
\begin{itemize}
|
||||
\item powerful timeseries database
|
||||
|
|
@ -23,8 +31,8 @@ Wait, what did I want to do again?
|
|||
\item fast paced environment
|
||||
\item low spatial resolution => privacy optimized
|
||||
\end{itemize}
|
||||
\image{.85\textwidth}{../../PresTeX/images/kibana}{Game trace in Kibana}{img:kibana}
|
||||
\image{.85\textwidth}{../../PresTeX/images/kibana2}{Game trace in Kibana}{img:kibana2}
|
||||
\image{\textwidth}{../../PresTeX/images/kibana}{Game trace in Kibana}{img:kibana}
|
||||
\image{\textwidth}{../../PresTeX/images/kibana2}{Game trace in Kibana}{img:kibana2}
|
||||
|
||||
|
||||
\section{Architecture}
|
||||
|
|
|
|||
|
|
@ -1,4 +1,4 @@
|
|||
\section{Setup für Kibana}
|
||||
\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}
|
||||
|
|
|
|||
|
|
@ -487,3 +487,12 @@ keywords = "Games, Agent based models, Simulations, Analytics"
|
|||
year={2011},
|
||||
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}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -47,7 +47,7 @@
|
|||
% Zur Auswahl der Sprache im folgenden Befehl
|
||||
% ngerman für deutsch eintragen, english für Englisch.
|
||||
%===============================================================================
|
||||
\selectlanguage{ngerman}
|
||||
\selectlanguage{english}
|
||||
\setstretch{1.1}
|
||||
%% Titelseite
|
||||
\maketitle
|
||||
|
|
|
|||
Loading…
Reference in New Issue