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@PHDTHESIS{Chakrabarti:958959,
author = {Chakrabarti, Arnab},
othercontributors = {Jarke, Matthias and Quix, Christoph and Lakemeyer, Gerhard},
title = {{E}xploratory pipeline for analysis and visualization of
large information spaces},
school = {RWTH Aachen University},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2023-05502},
pages = {1 Online-Ressource : Illustrationen, Diagramme},
year = {2023},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Dissertation, RWTH Aachen University, 2023},
abstract = {The advent of the fourth industrial revolution (Industry
4.0) has fueled the capability of today’s scientific and
commercial applications in generating, storing and
processing massive amounts of data. Visual exploration of
datasets having excessive data points and features leads to
over-plotting and visual clutter, resulting in the loss of
interpretability. This problem has been termed as “visual
noise” in the field of high-dimensional data exploration.
Our work in this thesis centers around the question of
reducing visual noise and improving data interpretability in
the quest of finding interesting patterns within
high-dimensional datasets. This task is highly challenged by
the so-called “curse of dimensionality”, which hinders
visual exploration as a high number of data dimensions are
required to be displayed on two-dimensional screen space. To
mitigate such challenges, we present a comprehensive and
flexible data exploration pipeline for handling
high-dimensional data. We incorporated the proposed pipeline
into the prototype “VizExplore”, which serves as an
automated exploratory tool for monitoring production
engineering processes. For the different stages of the
pipeline, firstly, we propose dimensionality reduction
methods for generating effective visualizations. With this,
we address the open research problem of performing data
reduction with the goal of minimizing information loss.
Second, we propose a visualization ranking model. We develop
novel methods of extracting visual characteristics from
images and design a generic evaluation framework capable of
comparing diverse visual structures in a uniform platform.
Finally, we ingest the knowledge gathered from the first two
layers and propose a visualization recommendation system
using data-specific characteristics and user intended
exploration tasks. We design recommendation engines using
both rule-based and knowledge-based approaches and highlight
the effectiveness of such recommenders in enhancing the
functionalities of the exploratory process, especially in
the field of high-dimensional data visualizations. We
evaluated our exploratory tool in various settings,
including data generated from engineering processes in the
Internet of Production (IoP). We highlight the usability of
our model in the IoP for handling high dimensional data and
generating effective visualization recommendations, in turn,
enhancing the decision-making capabilities for production
systems.},
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-2023-05502},
url = {https://publications.rwth-aachen.de/record/958959},
}
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