time

Advisor
Abstract

Information present in resumes has temporal as well as spatial dimensions, such as a person¿s work experience or education. This information by itself is comprehensible - it is very clear when, where, and for how long a person has worked or studied. It becomes increasingly difficult to maintain a clear overview when comparing multiple resumes, specifically their chronological information. Navigating back and forth between resumes, switching between views and attempting to compare every single event is highly inefficient. The most common and intuitive way to compare events from multiple resumes, would be to view them in a side-by-side fashion. Many similarities can be quickly recognized by doing this, but in cases where more than two resumes need to be compared simultaneously, the overview becomes cluttered, the amount of information increases substantially and comparing events becomes a difficult task. We propose the design and implementation of a web application - CV3 - that is capable of comparing multiple chronologies and visualizing the output in a clear manner, whilst maintaining a clean overview. Our approach supports users in filtering timeline events across all resumes, recognizing and extracting relevant information from resumes and visualizing their similarities efficiently in a single overview.

Year of Publication
2017
Secondary Title
Institute of Visual Computing and Human-Centered Technology
Paper
Number of Pages
75
reposiTUm Handle
20.500.12708/7386
Publisher
TU Wien
Place Published
Vienna
DOI
10.34726/hss.2017.43587
V. Filipov, “Visual exploration and comparison of multiple resume : focus on time and space”, Institute of Visual Computing and Human-Centered Technology. TU Wien, Vienna, p. 75, 2017.
Master Thesis
AC14475186
Author
Advisor
Abstract

This work has as its main focus the creation of resumes, how they are integrated into the hiring process, and how visual resumes play into the current world of recruitment and applications. On the side of applicants, there is a trend to create visualised resumes in the form of infographics whereas the recruiting side employs more and more automation technology that extracts the information contained in regular resumes and on social media sites in order to have to spend less time and therefore money on individualised resumes. Instead of viewing both these sides as opposites, this research tries to unify them and design a system that appeases both. The challenge is to find a way to create and use a new and innovative form of resume that benefits both recruiters and applicants. A new global specification created within a Community Group at the World Wide Web Consortium as well as a rudimentary implementation that can evolve into a rich ecosystem using that specification are introduced. Research for the topic had to be conducted from three different angles: the state of the art in research regarding recruitment and resumes, the state of the art in the industry, and currently existing resume standards, including the question whether they are suitable for the next-generation resume we are proposing. We are defining the core points of this next-generation resume that we labelled CV 2.0 and are proposing ways to reach a greater adoption of this resume in the future.

Year of Publication
2017
Secondary Title
Institute of Visual Computing and Human-Centered Technology
Paper
Number of Pages
90
reposiTUm Handle
20.500.12708/7393
Publisher
TU Wien
Place Published
Vienna
DOI
10.34726/hss.2017.32325
S. Bonic, “CV 2.0 - Global Resume”, Institute of Visual Computing and Human-Centered Technology. TU Wien, Vienna, p. 90, 2017.
Master Thesis
AC14475171
Author
Advisor
Abstract

More and more hospitals have been switching from paper-based patient records to electronic health records within the last years, which introduced new challenges for healthcare professionals. Each electronic record can hold a vast amount of diverse data, which can easily overwhelm a clinician in the stressful environment of a hospital. Consequently, important information can easily be missed or misinterpreted. These effects are especially critical for medication data, as medication errors can harm the treated patient directly. Proper information visualization is able to address these issues by providing cognitive support to healthcare professionals and facilitating insight into health records. The objectives of this thesis were to design a visualization of patient-specific medication histories and to implement an interactive prototype, which can be used in daily treatment settings. For this purpose, the characteristics and use cases of existing projects addressing

Year of Publication
2018
Secondary Title
Institute of Visual Computing and Human-Centered Technology
Paper
Number of Pages
111
reposiTUm Handle
20.500.12708/3501
Publisher
TU Wien
Place Published
Vienna
DOI
10.34726/hss.2018.54626
F. Filip, “Interactive Visualization of Medication Histories”, Institute of Visual Computing and Human-Centered Technology. TU Wien, Vienna, p. 111, 2018.
Master Thesis
AC15012066
Advisor
Co-Advisor
Abstract

The visualization and analysis of large graphs plays an essential role in various application fields. Since the size of graphs grew exponentially in the past few years, it became a challenge to reduce the visual clutter of dense and occluded graphs. By abstracting the structure of a node-link diagram, containing thousands of nodes and edges, visual clutter is reduced drastically, supporting the analysis of underlying patterns in an interactive approach. Additional visual techniques are used to overcome the challenge of representing the evolution of structural diagram changes and relationships between entities in dynamic graph visualization. The recent publications of large static and dynamic graph visualization techniques are using rich clients based on fast processing GPU algorithms, as well as distributed approaches for cluster-computing frameworks. Even though these techniques are capable of processing large-scale graphs interactively, they are also restricted by the user’s hardware or are more complex and expensive than simple client-server solutions. This thesis aims to provide an alternative approach, at providing a distributed, cross-platform, server-client application, able to visualize large node-link graphs, consisting of thousands of elements, interactively in a standard web-browser. We describe an aggregation strategy based on meta-elements, that provides an adjustable level of detail interface and visualizes the hierarchy of cumulative elements throughout multiple abstraction layers. By highlighting structural changes over time in dynamic graphs in combination with tools, such as panning and zooming and overview and detail, our system allows for dynamic graph exploration. We will demonstrate the usability of our technique by providing a complete prototype and present benchmarks on different graphs. Furthermore, we evaluate technical aspects of our approach as well as its applicability to large real-world graphs.

Year of Publication
2020
Secondary Title
Institute of Visual Computing and Human-Centered Technology
Paper
Number of Pages
94
reposiTUm Handle
20.500.12708/15621
Publisher
TU Wien
Place Published
Vienna
DOI
10.34726/hss.2020.73704
M. Reisacher, “Interactive web-based visualization of large dynamic graphs”, Institute of Visual Computing and Human-Centered Technology. TU Wien, Vienna, p. 94, 2020.
Master Thesis
AC15753180
Advisor
Co-Advisor
Abstract

The digitization of our world provides us with a vast amount of data. This data allows us to construct accurate models of real world situations which are explored and analyzed to get a deeper understanding and eventually draw conclusions for our further actions. Multivariate networks are a particularly complex construct which are ubiquitous in many different subject areas, like social media, telecommunication, transport, finance, and demographics. These networks often have a spatial context attached to them and usually evolve over time. This fact makes it even harder to efficiently visualize the many aspects of such a network. This thesis aims to define and build a visualization of a multivariate network which changes over time and space. The underlying data network is composed of real-world movement data of citizens of Vienna from 2007 to 2018, provided by the city of Vienna, MA23. This data represents the change of residencies of people moving to, from, or within Vienna. To tackle the complexity of the many dimensions of this data such as time, space, and other attributes, like the country of birth of the moving people, we follow a user-centered design approach proposed by Miksch et al. The implemented prototype of the visualization focuses on two different user groups, which are people from the department for urban development on the one hand and the public on the other hand. Both groups may take interest in the relations between the districts and in understanding the migration flow over the years. In the design process, we focus on strengths and weaknesses of different visualization techniques to amplify the visual expressiveness of the key aspects of the data. Spatial information is encoded in a geographic map on which flows depict movements between areas. The design choices of these flows are essential to sustain readability. The temporal aspects are depicted with different time-series visualizations. Each of them focuses on the data from a different angle. Interactivity and interoperability between these visualizations ensure determined navigation through the various aspects of the migration data. We evaluated the visualization prototype with five experts in the field of Visual Analytics and one non-expert. The evaluation showed that the right combination of different visualization and interaction techniques results in an effective and appropriate visualization from which users can draw the desired insight.

Year of Publication
2020
Secondary Title
Institute of Visual Computing and Human-Centered Technology
Paper
Number of Pages
112
reposiTUm Handle
20.500.12708/15054
Publisher
TU Wien
Place Published
Vienna
DOI
10.34726/hss.2020.72863
A. Scheidl, “Exploring networks over time and space utilizing visual analytics”, Institute of Visual Computing and Human-Centered Technology. TU Wien, Vienna, p. 112, 2020.
Master Thesis
AC15679330
Author
Advisor
Co-Advisor
Abstract

The research area "Knowledge-Assisted Visual Analytics" (KAVA) deals with the integration of domain knowledge into Visual Analytics approaches which offers many advantages for research as well as for the analysis of data since analysts do not need to rely on their domain knowledge and can concentrate more on the analysis task itself. Especially in the health care sector, KAVA has great potential which is currently not fully exploited since there are only a few approaches that deal with KAVA in combination with health care data. To fill this gap, we propose a new KAVA approach dealing with a dataset that resulted from a clinical trial of a medication for treating the eye disease Uveitis to provide the possibility of exploring and analyzing the dataset. For designing and developing the approach a user-centered design process, involving a domain expert, in combination with problem-driven visualization research is applied. The final approach is validated using a qualitative task-oriented user study with five visualization experts. The results suggest that the approach is able to support the analysis as well as exploration of the dataset.

Year of Publication
2021
Secondary Title
Institute of Visual Computing and Human-Centered Technology
Paper
Number of Pages
94
reposiTUm Handle
20.500.12708/17813
Publisher
TU Wien
Place Published
Vienna
DOI
10.34726/hss.2021.80701
L. Müllner, “Knowledge-assisted visual analytics: data exploration and insight generation of health care data”, Institute of Visual Computing and Human-Centered Technology. TU Wien, Vienna, p. 94, 2021.
Master Thesis
AC16230057
Author
Advisor
Co-Advisor
Abstract

With the increase of remote work due to COVID-19 and the overall movement towards open source projects, distributed version control system, like Git gained popularity overthe last years. The publicly available data on platforms (e.g., GitHub) therefore becomes richer and attracts sociologists and software analysts for further analysis.This master thesis aims to visualize GitHub trends using Visual Analytics. The data used originates from the GitHub API as well as GitHub Archive, is multivariate and contains different types of information containing repositories, users and events. This data will be extended by the temporal dimension to identify potential trends. For the problem definition and further methodology, the design triangle as described by Mikschet. al is being used.The outcome of the thesis is a prototype, that not only enables domain experts to fulfill common tasks related to identifying GitHub anomalies and trends but also allows foruser interaction to focus on more granular analysis. While many trends can potentially be visualized, this thesis will focus on a small subset of trends to introduce a generic approach and evaluate it on given scenarios and tasks. The general group of potential user groups is broad, but there is a strong emphasis on analysts in technology industries.The prototype was evaluated with domain experts in different fields of expertise that were asked to perform given tasks that can be fulfilled using the developed prototype. The results of the evaluation showed, that there is a strong interest in the analysis of GitHub data and that the right encodings and visualization methods can help find patterns and trends significantly.

Year of Publication
2021
Secondary Title
Institute of Visual Computing and Human-Centered Technology
Paper
Number of Pages
68
reposiTUm Handle
20.500.12708/18896
Publisher
TU Wien
Place Published
Vienna
DOI
10.34726/hss.2021.93182
T. Anderl, “Identifying GitHub trends using temporal analysis”, Institute of Visual Computing and Human-Centered Technology. TU Wien, Vienna, p. 68, 2021.
Master Thesis
AC16386639
Author
Advisor
Co-Advisor
Abstract

In art historical research, the study of social networks can provide insights into complex interactions between artists. However, despite the successful use of network visualizations in the field of art history, there are still many open questions as well as challenges that need to be solved in order to support art historians in the best possible way for their research. For example, existing approaches offer only limited possibilities to visually explore art historical networks with regard to their geographical context as well as their temporal development. Furthermore, so-called node-link diagrams are usually used for the visual representation of networks, which in the past revealed weaknesses in comparison with other representations in various studies. Finally, it is difficult to evaluate applications with respect to their suitability in the art history domain, since established evaluation approaches are often not conducted with domain experts. We present Exhibitions Explorer, a solution which enables the interactive, visual exploration of art historical networks. The solution integrates different visualization approaches from research into a new visualization concept that focuses on the requirements of the domain. Using an insight-based evaluation approach, we also demonstrate that, provided a suitable visualization concept, network visualizations can lead to a high quantity and quality of domain-relevant insights.

Year of Publication
2023
Secondary Title
Institute of Visual Computing and Human-Centered Technology
Paper
Number of Pages
123
reposiTUm Handle
20.500.12708/139675
Publisher
TU Wien
Place Published
Vienna
DOI
10.34726/hss.2023.99642
A. Nedas, “Utilizing visual analytics for network exploration in the domain of art history research”, Institute of Visual Computing and Human-Centered Technology. TU Wien, Vienna, p. 123, 2023.
Master Thesis
AC16736591
C. Bors, Eichner, C., Miksch, S., Tominski, C., Schumann, H., and Gschwandtner, T., “Exploring Time Series Segmentations Using Uncertainty and Focus+Context Techniques”, in EuroVis 2020, 2020.
A. Rind, Lammarsch, T., Aigner, W., Alsallakh, B., and Miksch, S., “TimeBench: A Data Model and Software Library for Visual Analytics of Time-Oriented Data”, IEEE Transactions on Visualization and Computer Graphics, Special Issue "VIS 2013", vol. 19, no. 12, pp. 2247-2256, 2013.