TU Ilmenau/AnLi Fotografie

Prof. Dr.-Ing. Steffen Strassburger

Visiting address:

Max-Planck-Ring 12 (Werner-Bischoff-Bau); Room F1120
D-98693 Ilmenau

Postal address:

Postfach 100 565
D-98684 Ilmenau

steffen.strassburger@tu-ilmenau.de

+49 (0) 3677 69-4051

Office hours

Please contact me by email for an appointment. I am also available for personal consultations (Skype, Webex, phone).

Entries in citation databases

Google Scholar

Research Gate

ACM Digital Library

Prof. Dr.-Ing. Steffen Strassburger

Visiting address:

Max-Planck-Ring 12 (Werner-Bischoff-Bau); Room F1120
D-98693 Ilmenau

Postal address:

Postfach 100 565
D-98684 Ilmenau

steffen.strassburger@tu-ilmenau.de

+49 (0) 3677 69-4051

Office hours

Please contact me by email for an appointment. I am also available for personal consultations (Skype, Webex, phone).

Entries in citation databases

Google Scholar

Research Gate

ACM Digital Library

Research Topics

  • Industry 4.0 and Digital Factory
  • Interoperability standards
  • Simulation methods (world views, hybrid simulation, model generation)
  • Distributed simulation and the High Level Architecture (HLA)
  • Digital twin, online simulation and simulation-based control centers

Memberships

  • Arbeitsgemeinschaft Simulation (ASIM) within Gesellschaft für Informatik (GI)
  • Society for Computer Simulation International (SCS)
  • Simulation Interoperability Standards Organization (SISO)
  • Association for Computing Machinery (ACM)
  • ACM - Special Interest Group on Simulation and Modeling (ACM SIGSIM)

Dissertations

Expertise as Referee

Academic Self-Government

  • Member of the Certification and Accreditation Commission (ZAK) (since 2020)
  • Vice Dean of the Department of Economic Sciences and Media (2016-2017)
  • Head of the Study Program Commission for Information Systems (2011-2016)
  • Deputy director of the Institute of Automotive and Production Engineering (2011-2014)
  • Examination board for information systems - member (2008-2016) and head (2008-2012)
  • Quality management representative of the Department of Economic Sciences and Media (2007-2011)
  • Member of the Senate Committee for University Development and Quality Assurance (2008-2011)

Work Experience and Education

  • seit 08/2018: Full professor at TU Ilmenau, Department of Mechanical Engineering, Head of the Group for Information Technology in Production and Logistics
  • 04/2007-07/2018: Full professor at TU Ilmenau, Department of Economic Sciences and Media, Head of the Group for Industrial Information Systems
  • 2003-2007: Head of Department, Fraunhofer Institute for Factory Operation and Automation, Department of Virtual Development, Magdeburg
  • 2001-2003: Research Associate, DaimlerChrysler AG, Research and Technology, Department for Product, Process and Resource Integration, Ulm, Germany
  • 11-12/1999: Research stay at the Georgia Institute of Technology, Atlanta, USA
  • 1998-2001: Research Assistant, Otto-von-Guericke-University Magdeburg, Department of Computer Science, Institute for Simulation and Graphics
  • 1995-1996: Study abroad, University of Wisconsin - Stevens Point, USA, sponsored by DAAD scholarship
  • 1992-1998: Study of Computer Science, Minor in Business Administration, Otto-von-Guericke-University, Magdeburg, Department of Computer Science

List of publications (since 2007 - only works according to the university bibliography of the TU Ilmenau)

Congress volume, monograph
Results: 3
Created on: Thu, 30 Jun 2022 23:42:49 +0200 in 0.0350 sec


Nissen, Volker; Stelzer, Dirk; Straßburger, Steffen; Fischer, Daniel
Multikonferenz Wirtschaftsinformatik (MKWI) 2016 : Technische Universität Ilmenau, 09. - 11. März 2016. - Ilmenau : Universitätsverlag Ilmenau
http://www.db-thueringen.de/servlets/DocumentServlet?id=27211
Hauser, Helwig; Straßburger, Steffen; Theisel, Holger
Simulation and visualization 2008 : proceedings of the 2008 Simulation and Visualization Conference, 28 - 29 February 2008. - Erlangen [u.a.] : SCS Publ. House, 2008. - XII, 351 S. ISBN 3-936150-53-2
Literaturangaben

Straßburger, Steffen;
Distributed simulation based on the high level architecture in civilian application domains. - Delft : SCS-Europe BVBA, 2001. - xviii, 224 Seiten. - (Advances in simulation ; 11)
Universität Magdeburg, Fakultät für Informatik, Dissertation 2001

ISBN 1-56555-218-0
Literaturverz. S. 125 - 140

Book and journal articles
Results: 11
Created on: Thu, 30 Jun 2022 23:42:49 +0200 in 0.0841 sec


Genath, Jonas; Bergmann, Sören; Straßburger, Steffen; Spieckermann, Sven; Stauber, Stephan;
Data farming and knowledge discovery in simulation data : development of an integrated solution
Data Farming und Wissensentdeckung in Simulationsdaten : Entwicklung einer integrierten Lösung. - In: Zeitschrift für wirtschaftlichen Fabrikbetrieb, ISSN 2511-0896, Bd. 117 (2022), 3, S. 144-150

Simulation als Methode der Digitalen Fabrik hat sich seit langem zur Unterstützung der Planung von Produktions- und Logistiksystemen etabliert. In Ergänzung zu bisher vorherrschenden Simulationsstudien wird bei der hier vorgestellten Methode der Wissensentdeckung in Simulationsdaten ein Simulationsmodell als Datengenerator verwendet. Dadurch können mittels Data-Mining- und Visual-Analytics-Methoden versteckte und potenziell nützliche Ursache-Wirkungs-Beziehungen in den generierten Daten aufgedeckt werden. Bislang fehlte es jedoch an integrierten Softwarelösungen für die Praxis.



https://doi.org/10.1515/zwf-2022-1032
Feldkamp, Niclas; Bergmann, Sören; Conrad, Florian; Straßburger, Steffen;
A method using generative adversarial networks for robustness optimization. - In: ACM transactions on modeling and computer simulation, ISSN 1558-1195, Bd. 32 (2022), 2, S. 12:1-12:22

The evaluation of robustness is an important goal within simulation-based analysis, especially in production and logistics systems. Robustness refers to setting controllable factors of a system in such a way that variance in the uncontrollable factors (noise) has minimal effect on a given output. In this paper, we present an approach for optimizing robustness based on deep generative models, a special method of deep learning. We propose a method consisting of two Generative Adversarial Networks (GANs) to generate optimized experiment plans for the decision factors and the noise factors in a competitive, turn-based game. In a case study, the proposed method is tested and compared to traditional methods for robustness analysis including Taguchi method and Response Surface Method.



https://doi.org/10.1145/3503511
Wörrlein, Benjamin; Straßburger, Steffen;
On the usage of deep learning for modelling energy consumption in simulation models. - In: Simulation Notes Europe, ISSN 2164-5353, Bd. 30 (2020), 4, S. 165-174

With the increasing availability of data, the desire to interpret that data and use it for behavioral predictions arises. Traditionally, simulation has used data about the real system for input data analysis or within data-driven model generation. Automatically extracting behavioral descriptions from the data and representing it in a simulation model is a challenge for these approaches. Machine learning on the other hand has proven successful in extracting knowledge from large data sets and transforming it into more useful representations. Combining simulation approaches with methods from machine learning seems, therefore, promising. Representing some aspects of a real system by a traditional simulation model and others by a model generated from machine learning, a hybrid system model (HSM) is generated. This paper discusses such HSMs and suggests a specific HSM incorporating a deep learning method for predicting the power consumption of machining jobs.



https://doi.org/10.11128/sne.30.tn.10536
Feldkamp, Niclas; Bergmann, Sören; Straßburger, Steffen;
Knowledge discovery in simulation data. - In: ACM transactions on modeling and computer simulation, ISSN 1558-1195, Bd. 30 (2020), 4, S. 24:1-24:25

This article provides a comprehensive and in-depth overview of our work on knowledge discovery in simulations. Application-wise, we focus on manufacturing simulations. Specifically, we propose and discuss a methodology for designing, executing, and analyzing large-scale simulation experiments with a broad coverage of possible system behavior targeted at generating knowledge about the system. Based on the concept of data farming, we suggest a two-phase process which starts with a data generation phase, in which a smart experiment design is used to set up and efficiently execute a large number of simulation experiments. In the second phase, the knowledge discovery phase, data mining and visually aided analysis methods are applied on the gathered simulation input and output data. This article gives insights into this knowledge discovery phase by discussing different machine learning approaches and their suitability for different manufacturing simulation problems. With this, we provide guidelines on how to conduct knowledge discovery studies within the manufacturing simulation context. We also introduce different case studies, both academic and applied, and use them to validate our methodology.



https://doi.org/10.1145/3391299
Bergmann, Sören; Straßburger, Steffen;
Automatische Modellgenerierung - Stand, Klassifizierung und ein Anwendungsbeispiel. - In: Ablaufsimulation in der Automobilindustrie, (2020), S. 333-347

Die automatische Modellgenerierung (AMG) ist ein Ansatz, der darauf abzielt, sowohl die Aufwände einer Simulationsstudie zu senken als auch die Qualität der erzeugten Modelle zu verbessern. Unter automatischer Modellgenerierung werden im Kontext der Simulation verschiedene Ansätze subsumiert, die es erlauben, Simulationsmodelle oder zumindest Teile von Simulationsmodellen mittels Algorithmen zu erzeugen. Eine umfassende Klassifizierung der Ansätze nach verschiedenen Merkmalen ist Ausgangspunkt weiterer Betrachtungen des Beitrags, in denen u. a. verschiedene technische Ansätze zur Modellgenerierung diskutiert werden. Weiterhin werden ergänzende Techniken, die die eigentliche Modellgenerierung flankierenden, wie z. B. die automatische Modellinitialisierung, diskutiert. Als ein möglicher Lösungsansatz wird beispielhaft ein Framework zur automatischen Modellgenerierung, -initialisierung und -adaption, welches das standardisierte Core Manufacturing Simulation Data (CMSD) Format als Basis nutzt, beschrieben.



https://doi.org/10.1007/978-3-662-59388-2_23
Bergmann, Sören; Feldkamp, Niclas; Straßburger, Steffen;
Emulation of control strategies through machine learning in manufacturing simulations. - In: Journal of simulation, ISSN 1747-7786, Bd. 11 (2017), 1, S. 38-50

Discrete-event simulation is a well-accepted method for planning, evaluating, and monitoring processes in production and logistics. To reduce time and effort spent on creating simulation models, automatic simulation model generation is an important area in modeling methodology research. When automatically generating a simulation model from existing data sources, the correct reproduction of dynamic behavior of the modeled system is a common challenge. One example is the representation of dispatching and scheduling strategies of production jobs. When generating a model automatically, the underlying rules for these strategies are typically unknown but yet have to be adequately emulated. In this paper, we summarize our work investigating the suitability of various data mining and supervised machine learning methods for emulating job scheduling decisions based on data obtained from production data acquisition. We report on the performance of the algorithms and give recommendations for their application, including suggestions for their integration in simulation systems.



http://dx.doi.org/10.1057/s41273-016-0006-0
Bergmann, Sören; Stelzer, Sören; Straßburger, Steffen;
On the use of artificial neural networks in simulation-based manufacturing control. - In: Journal of simulation, ISSN 1747-7786, Bd. 8 (2014), 1, S. 76-90

The automatic generation of simulation models has been a recurring research topic for several years. In manufacturing industries, it is currently also becoming a topic of high practical relevance. A well-known challenge in most model generation approaches is the correct reproduction of the dynamic behaviour of model elements, for example, buffering or control strategies. This problem is especially relevant in simulation-based manufacturing control. In such scenarios, simulation models need to reflect the current state and behaviour of the real system in a highly accurate way, otherwise its suggested control decisions may be inaccurate or even dangerous towards production goals. This paper introduces a novel methodology for approximating dynamic behaviour using artificial neural networks, rather than trying to determine exact representations. We suggest using neural networks in conjunction with traditional material flow simulation systems whenever a certain decision cannot be made ex ante in the model generation process due to insufficient knowledge about the behaviour of the real system. In such cases the decision is delegated to the neural network, which is connected to the simulation system at runtime. Training of the neural network is performed by observation of the real systems decision and based on the evaluation of data that can be gained through production data acquisition. Our approach has certain advantages compared to other approaches and is especially well suited in the context of on-line simulation and simulation-based operational decision support. We demonstrate the applicability of our methodology using a case study and report on performance results.



http://dx.doi.org/10.1057/jos.2013.6
Meyer, Torben; Straßburger, Steffen
Integrierte virtuelle Inbetriebnahme : erhöhte Skalierbarkeit durch Integration von Materialfluss- und Anlagensimulation. - In: Werkstattstechnik, ISSN 1436-5006, Bd. 103 (2013), 3, S. 177-183

In den vergangenen Jahren hat sich die virtuelle Inbetriebnahme als Methode der "Digitalen Fabrik" zur Verkürzung der realen Inbetriebnahmezeit und zum Softwaretest einzelner Maschinen oder Anlagen etabliert. Eine weitere Methode der Digitalen Fabrik ist die Materialflusssimulation; sie untersucht ganze Fabriken beispielsweise hinsichtlich produktionslogistischer Fragestellungen. An einem Prototyp wurden die Potentiale und Herausforderungen dargestellt, die infolge der Verbindung von Materialflusssimulation und virtueller Inbetriebnahme entstehen.



Taylor, Simon J. E.; Turner, Stephen J.; Straßburger, Steffen; Mustafee, Navonil;
Bridging the gap: a standards-based approach to OR/MS distributed simulation. - In: ACM transactions on modeling and computer simulation, ISSN 1558-1195, Bd. 22 (2012), 4, S. 18:1-18:23

In Operations Research and Management Science (OR/MS), Discrete Event Simulation (DES) models are typically created using commercial off-the-shelf simulation packages (CSPs) such as AnyLogic, Arena, Flexsim, Simul8, SLX, Witness, and so on. A DES model represents the processes associated with a system of interest. Some models may be composed of submodels running in their own CSPs on different computers linked together over a communications network via distributed simulation software. The creation of a distributed simulation with CSPs is still complex and typically requires a partnership of problem owners, modelers, CSP vendors, and distributed simulation specialists. In an attempt to simplify this development and foster discussion between modelers and technologists, the SISO-STD-006-2010 Standard for COTS Simulation Package Interoperability Reference Models has been developed. The standard makes it possible to capture interoperability capabilities and requirements at a DES modeling level rather than a computing technical level. For example, it allows requirements for entity transfer between models to be clearly specified in DES terms (e.g. the relationship between departure and arrival simulation times and input element (queue, workstation, etc.), buffering rules, and entity priority, instead of using specialist technical terminology. This article explores the motivations for distributed simulation in this area, related work, and the rationale for the standard. The four Types of Interoperability Reference Model described in the standard are discussed and presented (A. Entity Transfer, B. Shared Resource, C. Shared Event, and D. Shared Data Structure). Case studies in healthcare and manufacturing are given to demonstrate how the standard is used in practice.



https://doi.org/10.1145/2379810.2379811
Wack, Karl-Josef; Riegmann, Tobias; Straßburger, Steffen; Guenther, Ulrich
Mehr Sicherheit in der Fertigung : virtuelle Produktionsabsicherung in der Montage. - In: Digital-Engineering-Magazin, ISSN 1618-002X, (2011), 5, S. 46-47

Automobilhersteller müssen in der Lage sein, kürzere Innovations- und Produktlebenszyklen zu realisieren, um wettbewerbsfähig zu bleiben. Zusätzlich führen unterschiedliche Kundenanforderungen zu einer großen Zahl an Produktvarianten - und so zu einer steigenden Anzahl an Serienanläufen. Effiziente Produktionsanläufe gewinnen daher mehr und mehr an Bedeutung. Die Methoden und Werkzeuge der digitalen Fabrik erlauben bereits frühzeitig eine virtuelle Absicherung der Montage.




Conference papers
Results: 72
Created on: Thu, 30 Jun 2022 23:42:50 +0200 in 0.0948 sec


Genath, Jonas; Bergmann, Sören; Feldkamp, Niclas; Straßburger, Steffen;
Automation within the process of knowledge discovery in simulation data : characterization of the result data
Automatisierung im Prozess der Wissensentdeckung in Simulationsdaten : Charakterisierung der Ergebnisdaten. - In: Simulation in Produktion und Logistik 2021, (2021), S. 367-376
Literaturangaben

The traditional application of simulation in production and logistics is usually aimed at changing certain parameters in order to answer clearly defined objectives or questions. In contrast to this approach, the method of knowledge discovery in simulation data (KDS) uses a simulation model as a data generator (data farming). Subsequently using data mining methods, hidden, previously unknown and potentially useful cause-effect relationships can be uncovered. So far, however, there is a lack of guidelines and automatization-tools for non-experts or novices in KDS, which leads to a more difficult use in industrial applications and prevents a broader utilization. This paper presents a concept for automating the first step of the KDS, which is the process of characterization of the result data, using meta learning and validates it on small case study.



Rohacz, Anke; Straßburger, Steffen;
The acceptance of augmented reality as a determining factor in intralogistics panning. - In: Procedia CIRP, ISSN 2212-8271, Bd. 104 (2021), S. 1209-1214

In the automotive industry, an innovative tool to support the intralogistics planning is essential. One possibility is the use of augmented reality (AR). AR can be a suitable tool for improving the planning of intralogistics processes. An improvement of the intralogistics planning processes can be realized by applying this technology. The application thereby dependents on the acceptance of AR. In this paper, the Technology Acceptance Model and a survey are used to verify the acceptance of AR in intralogistics planning. In addition, relevant factors are identified having an impact on the acceptance using AR in intralogistics.



https://doi.org/10.1016/j.procir.2021.11.203
Kuehner, Kim Jessica; Scheer, Richard; Straßburger, Steffen;
Digital Twin: finding common ground - a meta-review. - In: Procedia CIRP, ISSN 2212-8271, Bd. 104 (2021), S. 1227-1232

The concept of the Digital Twin in the context of Industry 4.0 is omnipresent in research concerning manufacturing. However, the understanding of the term varies between applications. Although scholars have previously reviewed research on the topic, a consensus was never reached. Therefore, this paper attempts to compare existing reviews relating to the Digital Twin with the purpose of detecting prevalent as well as contrasting views on key issues. It elucidates commonalities in terminology, conceivable benefits as well as remaining research issues. Hence, it provides a conceptual outline of the Digital Twin that further research can build upon.



https://doi.org/10.1016/j.procir.2021.11.206
Scheer, Richard; Straßburger, Steffen; Knapp, Marc;
Concepts for digital-physical connection : comparison, benefits and critical issues
Digital-physische Verbundkonzepte: Gegenüberstellung, Nutzeffekte und kritische Hürden. - In: Simulation in Produktion und Logistik 2021, (2021), S. 11-20
Literaturangaben

Several concepts for digital-physical connection exist in literature and practice. This paper provides an overview over prevalent concepts. It characterises their specific attributes and places them in contrast with each other. Furthermore, it describes the major benefits as well as the most critical issues in the implementation of these concepts. These potential benefits and issues might then also serve as indicators for further research. From a practical perspective, this paper introduces a straightforward procedure to indicate the appropriate and most efficient concept for any specific implementation of a digital-physical connection system. It bases this indication on the specific requirements of the application.



Feldkamp, Niclas; Bergmann, Sören; Straßburger, Steffen;
Simulation-based deep reinforcement learning for modular production systems. - In: 2020 Winter Simulation Conference (WSC), (2020), S. 1596-1607

Modular production systems aim to supersede the traditional line production in the automobile industry. The idea here is that highly customized products can move dynamically and autonomously through a system of flexible workstations without fixed production cycles. This approach has challenging demands regarding planning and organization of such systems. Since each product can define its way through the system freely and individually, implementing rules and heuristics that leverage the flexibility in the system in order to increase performance can be difficult in this dynamic environment. Transport tasks are usually carried out by automated guided vehicles (AGVs). Therefore, integration of AI-based control logics offer a promising alternative to manually implemented decision rules for operating the AGVs. This paper presents an approach for using reinforcement learning (RL) in combination with simulation in order to control AGVs in modular production systems. We present a case study and compare our approach to heuristic rules.



https://doi.org/10.1109/WSC48552.2020.9384089
Wörrlein, Benjamin; Straßburger, Steffen;
A method for predicting high-resolution time series using sequence-to-sequence models. - In: 2020 Winter Simulation Conference (WSC), (2020), S. 1075-1086

With the increasing availability of data, the desire to interpret that data and use it for behavioral predictions arises. Traditionally, simulation has used data about the real system for input data analysis or within data-driven model generation. Automatically extracting behavioral descriptions from the data and representing it in a simulation model is a challenge of these approaches. Machine learning on the other hand has proven successful to extract knowledge from large data sets and transform it into more useful representations. Combining simulation approaches with methods from machine learning seems therefore promising to combine the strengths of both approaches. Representing some aspects of a real system by a traditional simulation model and others by a model incorporating machine learning, a hybrid system model (HSM) is generated. This paper suggests a specific HSM incorporating a deep learning method for predicting the anticipated power usage of machining jobs.



https://doi.org/10.1109/WSC48552.2020.9383969
Wörrlein, Benjamin; Straßburger, Steffen;
Sequence to Sequence Modelle zur hochaufgelösten Prädiktion von Stromverbrauch. - In: Proceedings ASIM SST 2020, (2020), S. 149-157

Modelling power consumption for jobs on a ma-chine tool is commonly performed by measuring the real power consumption of comparable jobs and machines. The so gathered data is then processed to represent the time-av-eraged sums of power consumptions of previous jobs. These values of power consumption are then used for upcoming comparable jobs. This approach allows for no high-resolution prediction of power consumption and further presumes static processing times of jobs. Here we propose a new approach to model power consumption that incorporates a Sequence-to-Sequence model, which generates time series according to dynamic data, that describes a numerical control code and environment settings such as state of tools, etc.



https://doi.org/10.11128/arep.59.a59021
Rohacz, Anke; Weißenfels, Stefan; Straßburger, Steffen;
Concept for the comparison of intralogistics designs with real factory layout using augmented reality, SLAM and marker-based tracking. - In: 53rd CIRP Conference on Manufacturing Systems 2020, (2020), S. 341-346

In the automotive industry, the intralogistics planning faces the problem of matching the planning data with the current conditions in the assembly hall. The large variety of parts leads to a constantly changing production. Based on this, we establish an approach for the comparison using augmented reality (AR) and simultaneous localization and mapping (SLAM). The use of SLAM enables the consistent application of AR in an assembly hall. Based on this, the objective of this article is to visualize 3D objects from the corresponding CAD planning tool in the real factory and thus the comparison of the intralogistics design with the real factory is possible due to AR. Nevertheless, there was a lack of practical implementations in intralogistics and therefore the concept is evaluated by two prototypical solutions. The first one is implemented on an iPhone 7 using SLAM. The second prototype is developed on a HoloLens 2 and is based on a hybrid tracking solution, SLAM and marker tracking.



https://doi.org/10.1016/j.procir.2020.03.039
Bergmann, Sören; Feldkamp, Niclas; Conrad, Florian; Straßburger, Steffen;
A method for robustness optimization using generative adversarial networks. - In: SIGSIM-PADS '20, (2020), S. 1-10

This paper presents an approach for optimizing the robustness of production and logistic systems based on deep generative models, a special method of deep learning. Robustness here refers to setting controllable factors of a system in such a way that variance in the uncontrollable factors (noise) has a minimal effect on given output parameters. In a case study, the proposed method is tested and compared to a traditional method for robustness analysis. The basic idea is to use deep neural networks to generate data for experiment plans and rate them by use of a simulation model of the production system. We propose to use two Generative Adversarial Networks (GANs) to generate optimized experiment plans for the decision factors and the noise factors, respectively, in a competitive, turn-based game. In one turn, the controllable factors are optimized and the noise remains constant, and vice versa in the next turn. For the calculations of the robustness, the planned experiments are conducted and rated using a simulation model in each learning step.



https://doi.org/10.1145/3384441.3395981
Feldkamp, Niclas; Bergmann, Sören; Straßburger, Steffen; Schulze, Thomas;
Visualization and interaction for knowledge discovery in simulation data. - In: Hawaii International Conference on System Sciences 2020, (2020), S. 1340-1349

Discrete-event simulation is an established and popular technology for investigating the dynamic behavior of complex manufacturing and logistics systems. Besides traditional simulation studies that focus on single model aspects, data farming describes an approach for using the simulation model as a data generator for broad scale experimentation with a broader coverage of the system behavior. On top of that we developed a process called knowledge discovery in simulation data that enhances the data farming concept by using data mining methods for the data analysis. In order to uncover patterns and causal relationships in the model, a visually guided analysis then enables an exploratory data analysis. While our previous work mainly focused on the application of suitable data mining methods, we address suitable visualization and interaction methods in this paper. We present those in a conceptual framework followed by an exemplary demonstration in an academic case study.



https://doi.org/10.24251/HICSS.2020.165