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@PHDTHESIS{Geisler:689438,
author = {Geisler, Sandra},
othercontributors = {Jarke, Matthias and Nicklas, Daniela},
title = {{A} systematic evaluation approach for data stream-based
applications},
school = {RWTH Aachen University},
type = {Dissertation},
address = {Aachen},
reportid = {RWTH-2017-04370},
pages = {1 Online-Ressource (x, 260 Seiten) : Illustrationen,
Diagramme},
year = {2016},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University 2017; Dissertation, RWTH Aachen University, 2016},
abstract = {The ubiquitous use of mobile devices, sensors, and the
linkage between them open up opportunities for new
applications using near real-time data processing and
analytics. Demands on information systems are high: huge
amounts of data have to be processed and results have to be
delivered in near real-time. These needs are tackled by the
field of Data Stream Management. Processing data streams
differs in many ways from static data set processing as much
of the data cannot be stored persistently. Likewise,
development methods for data stream-based applications have
to be specifically adapted. Process modeling has proved to
increase the quality of information systems, but there
exists no model specifically for data stream applications.
Furthermore, the production of high quality applications
requires means for a structured, iterative evaluation of the
application and its outcomes. Particularly, applications fed
by unreliable data sources, such as sensors, are prone to
quality losses and errors. Hence, measurement, monitoring,
and optionally the correction of data quality problems must
take a crucial part in the development and evaluation of
data stream applications. Data quality management needs to
be domain and application independent and smoothly
integrated into a data stream management system. These
requirements have not been met satisfactorily so far.We
counter the aforementioned issues by three main
contributions. First, we propose a process model
specifically tailored to the design, implementation, and, in
particular, for the evaluation of data stream applications.
To this end, we contribute a thorough analysis of data
stream management principles and technologies. We also
analyze existing process models in information management
and discuss their suitability to data stream applications.
Second, we propose evaluation methodologies embedded into
the process model. Along these methodologies we design and
implement a flexible evaluation framework for data stream
applications. Finally, we propose a methodology and
framework for data quality management for data stream
applications. We first analyze quality dimensions and
metrics relevant to data stream applications. We elicitate
existing data quality management methodologies and present a
methodology for data stream-based applications. As a major
contribution we implement a flexible, domain and application
independent data quality management framework for relational
data stream management systems based on the proposed
methodology.The process model and frameworks have been
developed and empirically validated and evaluated in the
context of two domains, namely Connected Intelligent
Transportation Systems and Mobile Health. Algorithmic
solutions for particular problems in the target domains have
been devised and applied. Iterative evaluations using the
proposed frameworks led to crucial optimizations of the
application results.},
cin = {121810 / 120000},
ddc = {004},
cid = {$I:(DE-82)121810_20140620$ / $I:(DE-82)120000_20140620$},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2017-04370},
url = {https://publications.rwth-aachen.de/record/689438},
}
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