000 Allgemeines, Informatik, Informationswissenschaft
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The e-commerce turnover has a constant growth rate of about 10%. An additional increase
in complexity and traffic spikes clarify the need for a scalable software architecture to prevent
a potential technical debt, higher financial cost, longer maintenance, or a reduced reliability.
Due to the fact, that existing approaches like the Palladio Approach require a high modelling
overhead and the importance of dropping this overhead was identified this master thesis is
focused on the modelling and simulation of e-commerce web application architectures using
a high-level approach to provide a faster, but possibly more inaccurate prediction of the
scalability.
This is done by the usage of the Design Science Research Process as a frame, a scientific
literature review for use of the existing knowledge base and the Conical Methodology for the
artefact creation. The artefact is a graphical model which is evaluated using a simulation
developed with Python and its framework SimPy. For model creation and evaluation a total
of twelve papers investigating the scalability of e-commerce web application architectures is
split into a test and train group. The training group and parts of the scientific research are
used to identify the components load balancer, application server, web tier, ERP system,
legacy system and database as well as some general characteristics that need to be considered.
The components with the most modelling variables are the application server and web
tier with a total of thirteen, while the ERP and legacy system only required five.
The model is evaluated using a total of three papers from the test group, where an average
throughput error of 5.78% and a response time error of 46.55% or 26.46% was identified. An
additional evaluation based on two non-e-commerce architectures shows the usability of the
model for other types of architectures. Even though the average error gives the impression,
that the model is not providing a good estimation, the graphical results show, that the model
and its simulation can be used to provide a faster scalability prediction. The model is least
accurate for the prediction of the situation, where the response time increases exponentially,
as this is the point, where variables, only accountable for some percentage and thus ignored
for the model, have the highest influence.
Future research can be found in the extension of the model by either adding or investigating
additional components, adding features ignored within this work or applying it to other
types of web application architectures. Additionally, both the low-level and the high-level
approaches can be brought together to combine the advantages from both approaches.
Medical imaging produces many images every day in clinical routine. Keeping up with the
daily image analysis task and this vast amount of data is quite a challenge for radiologists.
However, these analysis tasks can be automated with well-proven automatic segmentation
methods. Segmentation reviewing of an expert is necessary because learningbased
automatic segmentation methods may not perform well on exceptional image
data. Creating valid segmentations by reviewing them also improve the learning-based
methods.
Combining established standards with modern technologies creates a flexible environment
to efficiently evaluate multiple segmentation algorithm outputs based on different metrics
and visualizations and report these analysis results back to a clinical system environment.
The presented software system can inspect such quantitative results in a fast and intuitive
way, potentially improving the daily repetitive segmentation review and rework of a
research radiologist. The presented system is designed to be integrated into a virtual
distributed computing environment with other systems and analysis methods. Critical
factors for this particular environment are the handling of many patient data and routine
automated analysis with state of the art technology.
First experiments show that the time to review automatic segmentation results can be
roughly divided in half while the confidence of the radiologist is enhanced. The system
is also able to highlight individual slices which are essential for the expert’s review
decision. For this highlighting, different metric scores are compared and evaluated.