Lena Cibulski

Fraunhofer Institute for Computer Graphics Research

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Co-Authors

Bernhard Preim

Otto-von-Guericke University Magdeburg

Jörn Kohlhammer

Fraunhofer Institute for Computer Graphics Research

Thorsten May

Fraunhofer Institute for Computer Graphics Research

Hubert Mitterhofer

Linz Center of Mechatronics (Austria)

Henry Völzke

Universitätsmedizin Greifswald

Myra Spiliopoulou

Otto-von-Guericke University Magdeburg

Johanna Schmidt

Friedrich-Alexander-Universität Erlangen-Nürnberg

Jörn Kohlhammer

Fraunhofer Institute for Computer Graphics Research

Krešimir Matković

VRVis GmbH (Austria)

Uli Niemann

Otto-von-Guericke University Magdeburg

Cooperative Institutions

Otto-von-Guericke University Magdeburg

University of Zurich

Graz University of Technology

VRVis GmbH (Austria)

Fraunhofer Institute for Computer Graphics Research

University Hospital Magdeburg

TU Wien

Virginia Tech

Technical University of Darmstadt

Institut national de recherche en informatique et en automatique

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Visual Exploration and Analysis of Simulation and Testing Data in Motor Engineering

IEEE Computer Graphics and Applications (2024)

Patrick Louis Lena Cibulski Josef Suschnigg Edmund Marth Hubert Mitterhofer

End-of-line tests and defect detection are vital for ensuring the reliability of electric motors. However, automated defect detection methods (e.g., data-driven approaches) face challenges due to the limited availability of real data from failed motors. Simulated data, though beneficial, lacks the complexity of real motors, impacting the performance of these methods when applied to actual observations. To tackle this challenge, we introduce a visual analysis tool designed to facilitate the analysis of measured and simulated data, presented in the form of time series data. This tool helps identify domain-invariant features and evaluate simulation data accuracy, assisting in selecting training data for reliable automated defect detection in real-world scenarios. The main contribution of this work is a design proposal based on visual design principles, specifically tailored to address the unique requirements of electric motor professionals. The visual design is validated by findings from a think-aloud study with specialized engineers.

Data Exploration

10.1109/mcg.2024.3392969

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Citations (0)

Supporting Domain Characterization in Visualization Design Studies With the Critical Decision Method

Lena Cibulski Evanthia Dimara Setia Hermawati Jörn Kohlhammer

While domain characterization has become an integral part of visualization design studies, methodological prescriptions are rare. An underrepresented aspect in existing approaches is domain expertise. Knowledge elicitation methods from cognitive science might help but have not yet received much attention for domain characterization. We propose the Critical Decision Method (CDM) to the visualization domain to provide descriptive steps that open up a knowledge-based perspective on domain characterization. The CDM uses retrospective interviews to reveal expert judgment involved in a challenging situation. We apply it to study three domain problems, reflect on our practical experience, and discuss its relevance to domain characterization in visualization research. We found the CDM's realism and subjective nature to be well suited for eliciting cognitive aspects of high-level task performance. Our insights might guide other researchers in conducting domain characterization with a focus on domain knowledge and cognition. With our work, we hope to contribute to the portfolio of meaningful methods used to inform visualization design and to stimulate discussions regarding prescriptive steps for domain characterization.

Characterization

10.1109/visguides57787.2022.00007

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Citations (1)

How applicable are attribute-based approaches for human-centered ranking creation?

Computers & Graphics (2023)

Clara-Maria Barth Jenny Schmid Ibrahim Al-Hazwani Madhav Sachdeva Lena Cibulski

Item rankings are useful when a decision needs to be made, especially if there are multiple attributes to be considered. However, existing tools do not support both categorical and numerical attributes, require programming expertise for expressing preferences on attributes, do not offer instant feedback, lack flexibility in expressing various types of user preferences, or do not support all mandatory steps in the ranking-creation workflow. In this work, we present RankASco: a human-centered visual analytics approach that supports the interactive and visual creation of rankings. The iterative design process resulted in different visual interfaces that enable users to formalize their preferences based on a taxonomy of attribute scoring functions. RankASco enables broad user groups to (a) select attributes of interest, (b) express preferences on attributes through interactively tailored scoring functions, and (c) analyze and refine item ranking results. We validate RankASco in a user study with 24 participants in comparison to a general purpose tool. We report on commonalities and differences with respect to usefulness and usability and ultimately present three personas that characterize common user behavior in ranking-creation. On the human factors side, we have also identified a series of interesting behavioral variables that have an influence on the task performance and may shape the design of human-centered ranking solutions in the future.

Categorical variable

10.1016/j.cag.2023.05.004

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Citations (3)

PAVED: Pareto Front Visualization for Engineering Design

Computer Graphics Forum (2020)

Lena Cibulski Hubert Mitterhofer Thorsten May Jörn Kohlhammer

Abstract Design problems in engineering typically involve a large solution space and several potentially conflicting criteria. Selecting a compromise solution is often supported by optimization algorithms that compute hundreds of Pareto‐optimal solutions, thus informing a decision by the engineer. However, the complexity of evaluating and comparing alternatives increases with the number of criteria that need to be considered at the same time. We present a design study on Pareto front visualization to support engineers in applying their expertise and subjective preferences for selection of the most‐preferred solution. We provide a characterization of data and tasks from the parametric design of electric motors. The requirements identified were the basis for our development of PAVED , an interactive parallel coordinates visualization for exploration of multi‐criteria alternatives. We reflect on our user‐centered design process that included iterative refinement with real data in close collaboration with a domain expert as well as a summative evaluation in the field. The results suggest a high usability of our visualization as part of a real‐world engineering design workflow. Our lessons learned can serve as guidance to future visualization developers targeting multi‐criteria optimization problems in engineering design or alternative domains.

10.1111/cgf.13990

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Citations (25)

ITEA—interactive trajectories and events analysis: exploring sequences of spatio-temporal events in movement data

The Visual Computer (2016)

Lena Cibulski Denis Gračanin Alexandra Diehl Rainer Splechtna Mai Elshehaly

Abstraction

Data set

10.1007/s00371-016-1255-7

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Citations (4)

COMPO*SED: Composite Parallel Coordinates for Co-Dependent Multi-Attribute Choices

IEEE Transactions on Visualization and Computer Graphics (2022)

Lena Cibulski Thorsten May Johanna Schmidt Jörn Kohlhammer

We propose Composite Parallel Coordinates, a novel parallel coordinates technique to effectively represent the interplay of component alternatives in a system. It builds upon a dedicated data model that formally describes the interaction of components. Parallel coordinates can help decision-makers identify the most preferred solution among a number of alternatives. Multi-component systems require one such multi-attribute choice for each component. Each of these choices might have side effects on the system's operability and performance, making them co-dependent. Common approaches employ complex multi-component models or involve back-and-forth iterations between single components until an acceptable compromise is reached. A simultaneous visual exploration across independently modeled but connected components is needed to make system design more efficient. Using dedicated layout and interaction strategies, our Composite Parallel Coordinates allow analysts to explore both individual properties of components as well as their interoperability and joint performance. We showcase the effectiveness of Composite Parallel Coordinates for co-dependent multi-attribute choices by means of three real-world scenarios from distinct application areas. In addition to the case studies, we reflect on observing two domain experts collaboratively working with the proposed technique and communicating along the way.

sed

10.1109/tvcg.2022.3180899

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Citations (3)

A User-Centered Perspective on Information Needs of Stakeholders in the Circular Economy

Anna Preut Tobias Mertz Lena Cibulski Jan-Philip Kopka

Circular Economy

10.23919/egg62010.2024.10631223

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Citations (0)

Reflections on Visualization Research Projects in the Manufacturing Industry

IEEE Computer Graphics and Applications (2022)

Lena Cibulski Johanna Schmidt Wolfgang Aigner

The rise of Industry 4.0 and cyber-physical systems has led to an abundance of large amounts of data, particularly in the manufacturing industry. Visualization and visual analytics play essential roles in harnessing this data. They have already been acknowledged as being among the key enabling technologies in the fourth industrial revolution. However, there are many challenges attached to applying visualization successfully, both from the manufacturing industry and visualization research perspectives. As members of research institutions involved in several applied research projects dealing with visualization in manufacturing, we characterized and analyzed our experiences for a detailed qualitative view, to distill important lessons learned, and to identify research gaps. With this article, we aim to provide added value and guidance for both manufacturing engineers and visualization researchers to avoid pitfalls and make such interdisciplinary endeavors more successful.

Manufacturing

10.1109/mcg.2022.3156846

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Citations (6)

Visualizing Time Series Consistency for Feature Selection

Journal of WSCG (2019)

Lena Cibulski Thorsten May Bernhard Preim Jürgen Bernard Jörn Kohlhammer

Figure 1: Visualizing how consistently a variable subset captures the characteristics of a dependent variable.Segments corresponding to groups of comparable values on the subset are investigated (a).An inconsistency is brushed (b, c).Histograms (d) show that one of the variables (top) might add to the variable subset's discriminating ability.

Feature (linguistics)

10.24132/jwscg.2019.27.2.2

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Citations (2)

Subpopulation Discovery and Validation in Epidemiological Data

Shiva Alemzadeh Tommy Hielscher Uli Niemann Lena Cibulski Till Ittermann

10.2312/eurova.20171118

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Citations (10)