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[TASK] Create first graphic and include it into the document.

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Jan Philipp Timme 2016-09-13 18:16:26 +02:00
parent a709c49940
commit 0235a2aaf6
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*.out
*.synctex.gz
*.bak
*.pdf
Bachelorarbeit.pdf
*.lyx

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Bachelorarbeit.tex
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\usepackage{listings} % Codesnippets
\usepackage{scrhack} % Hack for lstlisting i suspect :-/
\usepackage{xcolor}
\usepackage{float}
\usepackage{verbatim} % für comment-environment
% Setup für Codeblocks
\lstset{
@ -162,9 +163,14 @@ Ereignisse haben für sich alleine betrachtet neben einer begrenzten Gültigkeit
Die Integration von \emph{Domänenwissen}\footnote{Hintergrundwissen für den Kontext der Ereignisverarbeitung, verändert sich während der Verarbeitung nur selten} ist ein weiterer Schritt, der die Brücke zwischen den aus komplexen Ereignissen gewonnenen Kenntnissen und bereits bekannten Fakten schlagen soll, um die gewonnenen Kenntnisse in einen eindeutigen Zusammenhang stellen und eine eindeutige Interpretation zu ermöglichen.
Um unter diesen Bedingungen viele Ereignisdatenströme mit hochfrequenten Ereignissen in nahezu Echtzeit zu verarbeiten ist \emph{CEP}\footnote{Complex-Event-Processing} das Mittel der Wahl: Mit CEP werden die Ereignisse der verschiedenen Datenströme für begrenzte Zeiträume im Speicher vorgehalten und innerhalb von sogenannten \emph{Sliding-Windows}\footnote{Mehr dazu in Kapitel~\ref{cpt:cep_intro}} betrachtet. Dabei können Ereignismuster erkannt werden und verschiedene Ereignisse aggregiert werden um neue komplexe Ereignisse zu erzeugen.
Um unter diesen Bedingungen viele Ereignisdatenströme mit hochfrequenten Ereignissen in nahezu Echtzeit zu verarbeiten ist \emph{CEP}\footnote{Complex-Event-Processing} das Mittel der Wahl: Mit CEP werden die Ereignisse der verschiedenen Datenströme für begrenzte Zeiträume im Speicher vorgehalten und innerhalb von sogenannten \emph{Sliding-Windows}\footnote{Mehr dazu in Kapitel~\ref{cpt:cep_intro}} betrachtet. Dabei können Ereignismuster erkannt werden und verschiedene Ereignisse aggregiert werden um neue komplexe Ereignisse zu erzeugen. Abbildung~\ref{fig:aggregation_patternmatching} stellt die Aggregation von Ereignissen sowie die Mustererkennung exemplarisch dar.
\todo{GRAFIK: Mustererkennung aus Sliding Window + Generation von neuem Event daraus}
\begin{figure}[htbp]
\centering
\includegraphics[width=\textwidth]{img/aggregation-patternmatching.pdf}
\caption{Aggregation und Mustererkennung auf einem Ereignisstrom}
\label{fig:aggregation_patternmatching}
\end{figure}
Ziel dieser Arbeit ist die Einführung in die Konzepte von CEP und RDF, sowie die Demonstration der praktischen Nutzung der CEP-Engine \enquote{C-SPARQL} zur Verarbeitung von RDF-Datenströmen am Beispiel einer Autoverleihgesellschaft zur Überwachung von Leihfahrzeugen. Auch soll ergründet werden, welche technischen Möglichkeiten existieren, um \emph{Reasoning} auf RDF-Datenströmen zu betreiben --- ein Prozess, durch den eine vorhandene Sammlung von Fakten auf Basis von vorgegebener Terminologie automatisch um daraus ableitbarem Wissen angereichert werden kann.
Diesbezüglich soll ergründet werden, welche CEP-Engines Reasoning bereits implementieren und wie weit ihre technischen Möglichkeiten reichen --- eine große Herausforderung, da die mit einzubeziehenden Ereignisdaten sich kontinuierlich verändern.

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