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INTEGRATING IMMERSIVE VIRTUAL ENVIRONMENT USER STUDIES INTO
ARCHITECTURAL DESIGN PRACTICE
A Pre-Occupancy User Study of Train Station Waiting Preferences with VREVAL
Grayson Bailey1
Olaf Kammler2
René Weiser3
Sven Schneider4
Ekaterina Fuchkina5
Bauhaus-Universität Weimar
Belvederer Allee 1, 99423 Weimar, GERMANY
1grayson.daniel.bailey@uni-weimar.de,
2olaf.kammler@uni-weimar.de,
3rene.weiser@uni-weimar.de,
4sven.schneider@uni-weimar.de,
5ekaterina.fuchkina@uni-weimar.de
ABSTRACT
User-centered studies in Immersive Virtual Environments (IVEs) are able to provide valuable feedback in
the form of Pre-Occupancy Evaluations (Pr-OEs). Pr-OEs allow for immersive design reviews of
architectural space before construction is complete, thus providing better opportunities for user-centered
values to be appraised and addressed by designers. If integrated into the architectural design process, Pr-
OEs can also aid in design development by comparing participant responses to multiple design variations.
In this paper we present VREVAL, a framework for performing user studies in Immersive Virtual
Environments (IVEs), along with an example of its application in the study of waiting preferences. An
overview of the VREVAL web platform and desktop application explain the modular study components
within a BIM workflow. An example study showcases the VREVAL “Placing” module while examining
participant waiting preferences in four alternative designs for a train station building in Germany.
Keywords: immersive virtual environment, architectural user study, waiting preference, Pre-Occupancy
Evaluation
1 INTRODUCTION
The use of Immersive Virtual Environments (IVEs) to perform user studies on architectural spaces has
become more popular in the last few years, especially on the subject of emergency wayfinding (Natapov et
al 2022, Irshad et al 2021). Such user studies allow for researchers and architects to understand qualitative
user responses which cannot be calculated, such as conditional preferences, real behavioral performances
and other participant responses to spatial experience. Previously, user studies were mainly performed as
Post-Occupancy Evaluations (POEs), which take place after the building is completed and in use. Today,
Bailey, Kammler, Schneider, Fuchkina, and Weiser
IVEs and related technologies allow for user studies to take place as Pre-Occupancy Evaluations (PrOEs),
which can integrate into the architectural design process.
Performing PrOEs during early design phases, especially during schematic design, also allows for the
evaluation of multiple design options and the selection of a design option based on specific data-driven
feedback. PrOEs have largely focused on the user interaction with digital mock-ups (Dunston et al 2011,
Crescenzio et al 2021), the comparison of user spatial ratings of interiors (Fisher-Gewirtzman et al 2019),
and the wayfinding behavior of users under different conditions (Natapov et al 2022, Irshad et al 2021).
However, there remain challenges which the organization of IVE studies must face, such as (1) providing
a general framework for customizable study design and execution, (2) easy implementation for researchers
and participants, (3) use of common research methods, and (4) ability for sharing full study structure and
environments (Wölfel et al 2021).
Several frameworks for IVE-based architectural research have been designed to address these various
challenges (Grübel et al 2017; Moloney et al. 2019; Agirachman and Shinozaki 2021), and common
research methods for wayfinding have been thoroughly developed, although there remains a lack of a
flexible multi-purpose study framework. Previously, VREVAL has been presented as a framework for a
range of user study focuses in IVEs (Schneider et al 2017), and has demonstrated the ability to combine
questionnaires, spatial ratings, and participant tracking in a robust framework. However, VREVAL was
also limited by the same issues of implementation ease, flexibility of study design, and ease of accessibility.
In further developing the VREVAL framework, effort has been made to construct a more modular, centrally
accessible, and easily integrated framework into commonly used Computer-Aided Architectural Design
(CAAD) and Building Information Modelling (BIM) methods for research study design.
In this paper, we present a technical overview of VREVAL in the form of a web platform and desktop
application, and contextualize the framework by the showing the design and outcomes of an example study
on train station waiting preferences. The description of VREVAL developments focus on the simplified
access via the web platform for researchers, the modular study elements and process of study design, and
the increased ease of study implementation and review. Integration with usual CAAD and BIM practices
will also be shown via the connection between study design, 3D modelling software, and results
visualization with the VREVAL Dynamo package. The example study will focus on user waiting
preferences in four design alternatives for a train station in Germany, combining both placing activities and
questionnaires in order to compare two forms of participant-provided responses. Qualitative and
quantitative outcomes from the study show VREVAL as not only a useful scientific tool, but also a useful
design tool for comparing architectural alternatives.
2 VREVAL
The workflow of VREVAL integrates user studies within the architectural design process, as seen in Figure
1. From a basic digital model of an architectural or urban design project, VREVAL user studies can be
designed to verify design parameters, test expected user behavior, and compare responses to multiple
designs. Once the study is conducted, the outcomes are reintroduced into the developing the architectural
or urban design project – either in the form of explicitly preferred design options, response data for
comparison against computational methods like Accessibility and Visibility Graph Analysis (VGA), or the
unveiling of further design problems which must be addressed.
Once a designer or researcher has a digital model, a new user study can be designed, administered, and
reviewed with VREVAL using two basic interfaces: a web platform with three main functions (study
design, administration, documentation), and a desktop application which also contains three functions
(study design, module testing, evaluation). Within the desktop application, two modes of use allow for
participants to perform user studies on a full range of devices. The Head-Mounted Display (HMD) Mode
uses Virtual Reality (VR) headsets and accessories, such as the Oculus Quest 2, as display and interface
Bailey, Kammler, Schneider, Fuchkina, and Weiser
devices, while the Monitor-Mouse-Keyboard (MMK) Mode allows for participation in studies without a
VR headset or accessories.
Figure 1: VREVAL workflow overview
The technical organization of VREVAL (Figure 2) centers on a database that stores study content, study
administration and study results. Via the web platform, the researcher uploads digital models and audio sets
and then organizes the study content into a sequenced set of experiences for the participant. Once a study
is ready, the desktop application downloads the study assets (digital models, instructional prompts, etc.)
and a participant performs tasks as specified by the researcher. Upon completion of each task, results are
uploaded to the VREVAL database in order for partial data to be saved even under the conditions of early
termination or participant cybersickness. Once the study is performed by the required number of
participants, the full set of results and documentation can be downloaded from the web platform. Finally,
these results can be further analyzed in statistical software and/or in 3D modelling software.
Figure 2: VREVAL technical organization
2.1 Study Content
In order to construct the study outlined above using VREVAL, there is a limited hierarchy of elements
which must be organized by the researcher. As seen in Figure 3, “Study Content” are basic elements (Forms,
Markers, Environment Bundles) at the base of the hierarchy, Organizational elements (Playlists, Scenarios,
Tasks) arrange how and when the participant performs actions during the study, and Administration
elements (Evaluation Groups, Sorting Questions, User Codes) help to conduct the study in a simple manner.
The three basic elements of Study Content are environment bundles (digital model and audio sets used in
the IVE), markers (specific locations within the IVE), and forms (text-based information and
questionnaires).
First of all, environment bundles contain all environmental data for the study (i.e. all 3d models of the
context, the design for study, audio to be heard during the study, etc.). 3D Models and audio sources are
first created via third party software (Autodesk Revit, Rhinoceros 3d, Audacity, etc.), and exported into
Unity. In Unity, the researcher separates 3D Models into sets based on their relation (i.e. urban context,
architectural design, scenery, etc.), which can be toggled within the study to differentiate which parts of the
Bailey, Kammler, Schneider, Fuchkina, and Weiser
environment bundles are included at any given moment. From Unity, these sets of models are exported,
zipped, and uploaded to the VREVAL web platform.
The second element of Study Content is a Marker, which represents a location in the IVE where an
interactive action takes place. In VREVAL there are three types of Markers: checkpoints, info points, and
gates. Checkpoints are Markers that are connected with goals of the study – locations where the participant
starts a task, receives information about a task, or ends a task. Info points, in contrast, are markers that
contain only warning information rather than task instructions, such as “go back to the Plaza to continue
the study”. Finally, Gates are a type of Marker that transports the participant from one point in the IVE to
another, simulating an elevator transporting the participant from one floor to another. Markers are placed
by the researcher using the design function of the VREVAL desktop application in either HMD or MMK
mode.
The final element of Study Content is the Form, which represent all text-based information provided to
participants. Forms can contain task instructions, questions (single or multiple answer), semantic
differentials, and can contain multiple pages for longer texts. The researcher organizes the content of Forms
directly on the VREVAL web platform.
Figure 1: VREVAL Element Hierarchy
2.2 Study Organization
Once the study content is provided by the researcher, the study organization is determined using the modular
elements of Tasks, Scenarios and Playlists. First, Task modules specify the type of activity which will be
performed by the participant, i.e. whether the participant will be placing a marker, providing annotation,
travelling to waypoints, or choosing among design options. Additionally, each Task specifies settings for
the user and environment, such as which environment bundles are included and how the user is able to
move around the IVE. Within each Task, Markers and Forms are combined and Task-specific settings are
chosen by the researcher, such as the waypoint locations during a Wayfinding Task or the number of
placements during a Placing Task.
The second organizational element of VREVAL is the Scenario. A Scenario is a collection of related Task
modules, and comprises a study section. Similarly, the third organizational element of VREVAL is the
Playlist, and Playlists are collections of Scenarios. In this way, the Scenario acts as a section of the study
and the Playlist determines in which order study sections are performed.
2.3 Study Administration
Once the study design is finished and the researcher has organized all elements in the VREVAL web
platform, the researcher must provide evaluation settings before the user study is activated and open for
participants. First, a “snap-shot” of the study is taken, making sure that no further changes are made to the
study while activated. Then, in the case of studies with multiple possible Playlists, the researcher must
Bailey, Kammler, Schneider, Fuchkina, and Weiser
determine Evaluation Groups which are linked with Playlists from the study. As an example, between-
group studies would require multiple Evaluation Groups with different playlists that contained changes in
their respective Scenarios or Tasks. The researcher must then provide a Sorting Question in order to evenly
distribute participants among the Evaluation Groups based on their answers.
Finally, once a user study is activated, unique User Codes are generated on the web platform. User Codes
are provided by participants in order to start the study, and are able to individually identify participant
sessions without betraying participant anonymity. While the study remains activated and after the study has
concluded, a full set of results and other documentation can be downloaded from the web platform. In the
following case study, we will describe the process of study design using VREVAL and how the framework
was applied in order to examine train station waiting preferences in multiple designs.
3 CASE STUDY
The following case study examines user waiting preferences within four train station building designs for
a small town in Germany. The Control option is the current construction on site, and three additional design
alternatives (Option A, B, C) have been provided by architectural students at the Bauhaus-Universität
Weimar. As seen in Figure 4, the four options differ in architectural form and services planning: Control
has a single building on the western side of the site with covered waiting, Option A has three tight buildings
framing a waiting area on the eastern side of the site, Option B has a single building on the eastern side of
the site and covered waiting along Platform 1, and Option C has a three buildings aligned along the platform
with covered waiting along Platform 1.
Figure 4: Four design options studied
The goal of this case study is to document waiting location preferences in each design, compare these
preferences in reference to architectural qualities, and query qualities of importance for user waiting
experience. Waiting preferences are particularly important in the architectural design of transit buildings,
as the behavior and preferences of users will determine the success of building services and commercial
areas. User experience in train travel is usually most stressful for travelers’ while changing trains and
waiting on travel connections (Van Hagen 2011, Van Hagen and Bron 2014), thus understanding the
preferred waiting behavior of users is very relevant to the transit architecture. However, previous seating
preference research has focused the correlation of seat location and educational performance (Gou et al
2018, Tunahan et al 2021), or seating choices and user profiles (Clark and Walker 2020). With combined
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questionnaires and placing tasks, the specific locations of waiting preference can now be documented with
VREVAL, and thus provide direct locations of preference within architectural models.
In this study, waiting preference is examined for each of the design options with two methods, as seen in
Figure 5. The first method (1) asks participants to place a marker at 3 locations that they would prefer to
wait while at the train station. The participant is then able to wander the design option in order to choose
three preferred locations. The second method (2) queries which qualities of a location are most important
when choosing where to wait.
Figure 5: Option A Tasks T1 and T2 with starting locations highlighted.
In applying these two methods, VREVAL requires two corresponding tasks: the Placing Task (T1), and a
Default Task (T2). In T1 participants are placed in the waiting area of a single design option and asked to
place a marker at three locations that they would most like to wait for their train using the form
“Waiting_Placement_Control” (Figure 6). T1 requires the researcher to select the required environment
settings, user settings, along with a starting Marker (“Waiting_Control”) and starting Form (“Waiting
Placement Intro”), as seen in Figure 7.
Figure 6: VREVAL Forms used in Tasks T1 (Left) and T2 (Right).
T2 also places participants in the waiting area of the respective design before asking them to answer a
multiple-choice question by selecting one or more from a list of waiting area qualities (Figure 6). In
selecting content for T2, the same marker “Waiting_Control” is chosen, although linked instead with the
qualities questionnaire form “Waiting_Placement_FollowUp” (Figure 7).
Bailey, Kammler, Schneider, Fuchkina, and Weiser
Figure 7: VREVAL Scenario “Waiting Placement-Control” containing two tasks: Placing Task
(Left), and Default Task (Right).
As this is a between-group study, multiple Tasks, Scenarios, and Playlists are required in order to provide
four study variants for Control and Options A, B, and C. Although much of the study Markers and Forms
are identical in each variant, different environment bundles are required.
Finally, four evaluation groups were created (Control, Option A, Option B, and Option C) based on the
different Playlists available, and participants were sorted using the question: “Is this your first year of
university?”. In this way, the research team distributed the youngest and least academically experienced
participants evenly throughout each evaluation group.
3.1 Study Sequence
A full study sequence (Figure 8) was created by the research team in order to regulate participant experience
and was conducted by a student assistant. The waiting preference study was a part of a larger multi-section
study conducted from November 24, 2021 and January 12, 2022 (Bailey et al 2022). The first phase [A]
involved an introduction to the user study and the participant providing their informed consent. The second
phase [B] had the participant wear the HMD and perform both a VREVAL tutorial of actions and the
Playlist of VREVAL Scenarios for the full user study (waiting preference highlighted in Figure 8).
Afterwards, the final phase [C] involved a quick debrief before the participant exited. Finally, the student
attendant sanitized the equipment and switched out the HMD for the next participant. The full study
sequence took approximately 45 – 60 minutes per participant, while the waiting preference section took 5-
10 minutes on average.
Figure 8: Study Sequence
In total, 62 participants (47 feminine / weiblich, 14 masculine / männerlich, 1 non-binary / divers) were
involved in the study, with sorted between-groups (Control = 16, Option A = 15, Option B = 16, Option C
= 15). Most participants were bachelor or masters students at the Faculty of Architecture and Urbanism at
Bauhaus-Universität Weimar.
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4 RESULTS
Results from all participants were downloaded from the web platform at the end of the study period
(December 2021 – January 2022). Questionnaire responses were analyzed using statistical software, while
the placed locations of preference were visualized in Autodesk Revit using the VREVAL Dynamo package
provided on the web platform.
4.1 Placing Task
Participant responses for preferred waiting locations were visualized in Autodesk Revit in order to show
points of clustering, as marked in Figure 9.
Figure 9: Placement results for T1 in all Evaluation Groups.
Clusters were counted as 6 or more markers placed within a 3 meter radius. Two cluster locations were
found in Control, one at a covered bench placed along Platform 1 and one at an uncovered bench by the
bicycle racks along Platform 1. Option A saw three cluster locations: one location at a bench along Platform
1 backed by a brick wall, one at the edge of the waiting court along Platform 1 and the Café area, and one
in the array of benches within the waiting court. Option B saw dominant clusters within the seating bays
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placed along Platform 1, with three out of four of the bays containing a cluster. Option C saw a more
distributed set of locations with only one cluster between the center building and Platform 1. However, the
presence or lack of clusters in each of the design options cannot be seen as positive or negative, as waiting
distribution must be evaluated based on the coherence of design parameters (i.e. design intention, relation
of locations to the design area, etc.).
Participant placements are also visualized based on their sequence (Figure 9) with green totems representing
first choices, gold totems as second choices, and magenta totems as third choices. Although there were
slightly elevated percentages of second and third choices outside of the station building area (marked with
a red dashed line in Figure 9), distribution of sequenced locations shows little actionable data for
determining first preferences in any of the design options. However, future analysis of pathways taken
between sequenced placements could add in additional understanding of wayfinding behavior while looking
for waiting locations.
Finally, demarcated areas (full design area, unprogrammed exterior area, station services area, interior
waiting area) were used to understand the placing behavior of participants within the train station. As Figure
9 shows, participant responses broadly preferred the unprogrammed exterior areas (areas without prior
function, i.e. bicycle storage, parking, etc.). The distribution of participants throughout this exterior area
was dependent on architectural form, with the open planning of Option C providing more even distribution
than the closed court form of Option A. Control and Option had the highest proportion of participants
choose locations in the unprogrammed exterior area, while all options retained the vast majority of
participants within the full design area.
In each design option, only 1 participant chose to wait within the interior waiting area, showing that
participants mostly preferred exterior waiting areas with the most direct visual relation to Platform 1.
However, no formal language was used in describing weather conditions or temperature, so future research
must engage with further environmental conditions to understand waiting preferences under various
circumstances.
4.2 Qualities Questionnaire
In response to the follow-up question of Task 2 (“Which qualities do you find most important for a waiting
area in a train station?”), participant responses in total show a distinct prioritization of “Visibility” (81%)
and “Safety” (71%). As seen in Table 1, these two broadly important qualities were met by a more uneven
set of responses to the remaining qualities.
While overall qualities of influence were registered in all design options, some responses showed
dependence on the between-group. Interestingly, the quality “Activity” received majority responses (63%)
in Option C, and “Viewpoint” received a majority of responses (67%) in Control. Option C might have
highlighted “Activity” by the distribution of planter elements, thus increasing participant response through
a demonstration of the quality. In contrast, the choice of “Viewpoint” in Control – an option with limited
viewpoints – might have been in reaction to the absence of the quality.
“Aesthetics” was found to be more important to participants in Options B (60%) and received a majority of
responses, while receiving less than a majority in Control (27%) and Options A (19%) and C (44%). It is
notable that Options B and C are more stylized, thus possible that “Aesthetics” was valued higher when
more noticeably present, but otherwise not a strong indicator of waiting preference. The value of “Services”
was rated quite average in Control (53%) and Option A (44%), and lower in Options B (20%) and C (19%).
The list of qualities provided seemed adequate, as very low responses were given to “Other Reasons…”.
However, it is clear that the framing of qualities might have an interpretive effect on participants. Under
the condition of selecting “Other Reasons…”, the study attendant asked the participant to explain their
reasons and marked these down in the study log. While a majority of these responses were conceptually
Bailey, Kammler, Schneider, Fuchkina, and Weiser
similar to already present qualities on the list, “green space” appeared twice in these responses as a reason
for choosing a location to wait.
Table 1: Important Qualities For Waiting (T2) Responses from all Evaluation Groups
5 DISCUSSION
In this paper we have presented the design and execution of a waiting preference user study using the
updated VREVAL framework, showing how to integrate VREVAL user studies into the architectural
design process, and how to utilize VREVAL user studies in making design decisions. The development of
the VREVAL web platform provides centralized researcher access to modular study design, administration
and review, which limits the requirement for researchers to maintain specific software and programming
knowledge. VREVAL also simplifies the processes of study implementation via the desktop application in
either HMD or Monitor Mode.
In this study, four train station design options were compared in terms of waiting preference, and while
there is no simple “best option” in the study results, the participant chosen locations for waiting show
behavioral differences based on architectural form and station planning. These results provide the general
preference for station building designs that distribute waiting areas along Platform 1 and combine direct
visual connections with material boundaries. Further, responses to the follow-up questionnaire support
these assumptions by highlighting the shared waiting preference qualities of “Visibility” and “Safety” of
most participants. While a large portion of recent IVE-based architectural studies have concentrated on
wayfinding behavior (Ewart and Johnson 2021, Arias et al 2021, Zhang et al 2021), this study has also
shown the possibility of appraising locational preferences via placing tasks and questionnaires situated in
IVEs.
Further, future analysis of waiting preferences from this study and others should combine participant
responses from VREVAL with locational analysis of visibility and centrality. Combining preferred
locations from the IVE with Isovist analysis, centrality analysis and other available computational tools
will allow for a preliminary modelling of waiting qualities to be used as a predictive tool.
5.1 Outlook
Future work in regards to user waiting preferences must focus on performing more studies for a better
developed understanding of behavior and preference in a variety of project types and spatial organizations.
Additionally, several technical topics within IVE study design remain unclear in term of their influence on
participant responses:
Level of Detail (LOD) in IVEs and methods of Material Representation must be understood in the
ways that they effect participant immersion and distance perception.
Environmental Conditions (Weather, Time of Day, etc.) must be included in studies that assess
participant preferences, especially in exterior spaces. Previous research has already show the utility
Multiple Choice Answers Control Option A Option B Option C Total
The area must be an interesting space (Aesthetics)4 (27% )3 (19% ) 9 (60% )7 (44% )23 (37% )
The area must have a visual connection to platforms and services (Visibility)15 (100%)14 (88% )11 (73% )10 (63% )50 (81% )
The area must have a proximity to services (Se rvices)8 (53%)7 (44% )3 (20% )3 (19% )21 (34% )
The area must have an enjoyable view (Viewpoint)10 (67% )7 (44% )6 (40% )7 (44% )30 (48% )
The area must feel very safe (Safety)9 (60% )13 (81% )12 (80% )10 (63% )44 (71% )
The area must have a high amount of seating (Availabili ty)7 (47% )6 (38% )4 (27% )8 (50% )25 (40% )
The location must be restful and calm (Activity)7 (47% )7 (44% )6 (40% )10 (63% )30 (48% )
Other reasons… 0 1 (6% )1 (7%) 3 (19% )5 (8% )
Bailey, Kammler, Schneider, Fuchkina, and Weiser
of IVE-based studies on lighting conditions, and other environmental aspects that are difficult to
control in real-world settings (Heydarian et al 2015, Krösl et al 2018).
Static or dynamic use of entourage must be understood in how it affects participant wayfinding and
preference behavior. Current research shows that animated crowds have an effect on participant
behavior (Yassin et al 2021), although more research must be done on how and when to mitigate
these effects.
Further, there are topics of research which must address the IVE and narrative methodologies used. The
most studied of these topics is the difference between IVE and real world participant responses (Interrante
et al 2006, Clemenson et al 2020). For the most part, there is now general acceptance of parity between IVE
and real world experiments in Wayfinding and Questionnaires (Ewart and Johnson 2021, Wagener et al
2020), but other tasks (Placing, Pointing, Distance Perception, etc.) must be shown to be reliable within
IVE-based studies. Also, the narrative devices used in IVE-based studies is a future field of research that
should engage what level of background information is required for participant understanding, what
narrative basis is best for user study requirements, and generally what specific language is best used when
describing virtual actions and spaces.
Finally, a set of best practices for VREVAL and other IVE frameworks must be developed in tandem with
further studies in order to maintain the highest level of impartiality and reproducibility possible. However,
much of the best practices remains on the discretion and knowledge of the researcher, as a highly flexible
framework for IVE-based user studies cannot be seen as the limiter of biases.
ACKNOWLEDGMENTS
This study is part of the project OpenVREVAL, in cooperation with Deutsche Bahn Station & Service AG
and funded by the Thüringer Ministerium für Wirtschaft Wissenschaft und Digitale Gesellschaft
(TMWWDG). Special thanks are given to Paul Leon Pollack for study assistance, and to the students from
Bauhaus-Universität Weimar for their creative input and investigative spirit.
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AUTHOR BIOGRAPHIES
SVEN SCHNEIDER is Interim Professor and Chair at the Professur Informatik in der Architektur at
Bauhaus-Universität Weimar: sven.schneider@uni-weimar.de.
OLAF KAMMLER is a Research and Teaching Assistant at the Professur Informatik in der Architektur
at Bauhaus-Universität Weimar: olaf.kammler@uni-weimar.de.
GRAYSON BAILEY is a Research Assistant at the Professur Informatik in der Architektur at Bauhaus-
Universität Weimar: grayson.daniel.bailey@uni-weimar.de.
RENÉ WEISER is a software developer at the Professur Informatik in der Architektur at Bauhaus-
Universität Weimar: rene.weiser@uni-weimar.de.
EKATERINA FUCHKINA is a Research and Teaching Assistant at the Professur Informatik in der
Architektur at Bauhaus-Universität Weimar: ekaterina.fuchkina@uni-weimar.de.
