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


+49 (0) 3677 69-4051

Office hours

Within the lecture period, you can typically meet me in my office without an appointment on Wednesdays between 2:00 p.m. and 4:45 p.m. Otherwise 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


  • 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)


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: 6
Created on: Tue, 16 Apr 2024 23:20:26 +0200 in 0.0534 sec

Wenzel, Sigrid; Rabe, Markus; Straßburger, Steffen; Viebahn, Christoph von
Energy-related material flow simulation in production and logistics. - Cham : Springer International Publishing, 2024. - 1 Online-Ressource (xiii, 203 p.) ISBN 978-3-031-34218-9

Classification, input data, and key performance indicators -- Manufacturing -- Automotive -- Transportation -- Retail -- Perishables -- Renewables.

Bergmann, Sören; Feldkamp, Niclas; Souren, Rainer; Straßburger, Steffen
Simulation in Produktion und Logistik 2023 : ASIM Fachtagung : 20. Fachtagung, 13.-15. September 2023, TU Ilmenau. - Ilmenau : Universitätsverlag Ilmenau, 2023. - 1 Online-Ressource (XII, 485 Seiten). - (ASIM-Mitteilung ; Nr. 187)
Straßburger, Steffen
DaWiS - Entwicklung einer integrierten Lösung für das Data Farming und die Wissensentdeckung in Simulationsdaten : Abschlussbericht des im Rahmen der Fördermaßnahme KMU-innovativ: IKT geförderten Verbundprojekts : (Projektlaufzeit: 01.01.2020-31.12.2021). - Ilmenau : Technische Universität Ilmenau - Fachgebiet Informationstechnik in Produktion und Logistik (ITPL). - 1 Online-Ressource (31 Seiten, 2,81 MB)Förderkennzeichen BMBF 01 IS 190 42B

Nissen, Volker; Stelzer, Dirk; Straßburger, Steffen; Fischer, Daniel
Multikonferenz Wirtschaftsinformatik (MKWI) 2016 : Technische Universität Ilmenau, 09. - 11. März 2016. - Ilmenau : Universitätsverlag Ilmenau
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

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: 13
Created on: Tue, 16 Apr 2024 23:20:26 +0200 in 0.1047 sec

Rabe, Markus; Stoldt, Johannes; Straßburger, Steffen; Viebahn, Christoph von
Classification, input data, and key performance indicators. - In: Energy-related material flow simulation in production and logistics, (2024), S. 3-23

Simulation is a well-known technology for production and logistics, especially for the planning of new systems and the examination of ideas to optimize existing ones. In the past, the main target of such studies has been costs of equipment and personnel, but the continuously stricter view on consumption of energy has shifted this focus towards the analysis of energy consumption and emission of greenhouse gas. In some cases this might be straightforward, e.g., when the resulting production hours can just be multiplied with energy consumption per hour. Many cases, however, are far more complicated and can only be sufficiently analyzed when the detailed dynamics of energy consumption are already considered in the simulation model. Thus, a number of different approaches exist to model energy aspects in simulation models, depending on the goal of the investigation and the kind of production or logistics process. This chapter classifies these approaches in a morphological box and explains the details of the related categories. Furthermore, it discusses the requirements to input data that arise when simulation models are amended with energy components, and discusses the additional results that can be gained from such models.

Straßburger, Steffen;
Die ereignisdiskrete Simulation und ihr Verhältnis zu informationstechnischen Modetrends. - In: Drei Dutzend Jahre Simulationstechnik, (2022), S. 5-6

Dem Motto dieses Festkolloquiums entsprechend schaut dieser Beitrag auf die Methode der ereignisdiskreten Simulation und ihr Verhältnis zu informationstechnischen Modetrends im Betrachtungszeitraum. Im Grunde argumentieren wir, dass sich an der Methode der ereignisdiskreten Simulation im Betrachtungszeitraum - also in den letzten 36 Jahren - nichts Wesentliches geändert hat. Dies ist nicht als Kritik an der ereignisdiskreten Simulation zu werten, sondern könnte heute als Resilienz bezeichnet werden. Die ereignisdiskrete Simulation ist weiterhin hochrelevant und zeigt im Zusammenspiel mit informationstechnischen Neuerungen ihr nach wie vor großes Nutzenpotential.

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.

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.

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.

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.

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.

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.

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.

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.

Conference papers
Results: 81
Created on: Tue, 16 Apr 2024 23:20:27 +0200 in 0.1015 sec

Genath, Jonas; Straßburger, Steffen
How not to visualize your simulation output data. - In: 2023 Winter Simulation Conference (WSC), (2023), S. 1351-1362

Hybrid modeling and simulation studies combine well-defined methods from other disciplines with a simulation technique. Especially in the area of output data analysis of simulation studies, there is great potential for hybrid approaches that incorporate methods from machine learning and AI. For their successful application, the analytical capabilities of machine learning and AI must be combined with the interpretive capabilities of humans. In most cases, this connection is achieved through visualizations. As methods become more complicated, the demands on visualizations are increasing. In this paper, we conduct a data farming study and delve into the analysis of the output data. In doing so, we uncover typical errors in visualizations making the interpretation and evaluation of the data difficult or misleading. We then apply concepts of visual analytics to these visualizations and derive general guidelines to help simulation users to analyze their simulation studies and present results unambiguously and clearly.

Scheer, Richard; Straßburger, Steffen; Knapp, Marc
The Hybrid Digital Twin: a practical way to connect simulation with operational production systems :
Der Hybride Digitale Zwilling: eine praxistaugliche Verbindung von Simulation und operationellen Produktionssystemen. - In: Simulation in Produktion und Logistik 2023, (2023), S. 91-101

Digital Twins are currently a topic of much discussion in academia. However, they have yet to be transferred to general industrial practice because there are still significant challenges concerning their implementation. This paper proposes the concept of the Hybrid Digital Twin to address these challenges. At first, it will elucidate the concept and introduce a real-world prototype of an operational production line. Afterwards, it will validate the prototypical implementation and demonstrate a detailed strategy to calibrate it. Then the paper presents potential strategies to use the Hybrid Digital Twin in a production environment. Finally, further developments and remaining issues are discussed.

Feldkamp, Niclas; Straßburger, Steffen
From explainable AI to explainable simulation: using machine learning and XAI to understand system robustness. - In: ACM SIGSIM-PADS 2023, (2023), S. 96-106

Evaluating robustness is an important goal in simulation-based analysis. Robustness is achieved when the controllable factors of a system are adjusted in such a way that any possible variance in uncontrollable factors (noise) has minimal impact on the variance of the desired output. The optimization of system robustness using simulation is a dedicated and well-established research direction. However, once a simulation model is available, there is a lot of potential to learn more about the inherent relationships in the system, especially regarding its robustness. Data farming offers the possibility to explore large design spaces using smart experiment design, high performance computing, automated analysis, and interactive visualization. Sophisticated machine learning methods excel at recognizing and modelling the relation between large amounts of simulation input and output data. However, investigating and analyzing this modelled relationship can be very difficult, since most modern machine learning methods like neural networks or random forests are opaque black boxes. Explainable Artificial Intelligence (XAI) can help to peak into this black box, helping us to explore and learn about relations between simulation input and output. In this paper, we introduce a concept for using Data Farming, machine learning and XAI to investigate and understand system robustness of a given simulation model.

Wörrlein, Benjamin; Straßburger, Steffen
Dynamic Time Warping und Synthesedaten zur Validierung von Seq2Seq für die Simulation. - In: ASIM Workshop 2023, (2023), S. 133-142

Seq2Seq is a machine learning method that allows to translate sequences into other sequences. This method has been tried in hybrid simulation of machine tools. The method has been used to generate time series of energy consumption of jobs from the corresponding numerical control code that runs on a machine tool. Seq2Seq suffers from various problems. Firstly, the creation of training data is costly. Secondly, standard Seq2Seq metrics only allow for the evaluation of a prediction of one timestamp at a time, not an entire time series. Thirdly, training metrics are failing when vanilla data is used, as two identical numerical control codes can result in deviating time series. This causes confusion for the model in the training loop, as it is not clear which time series should be considered correct. Here we propose a holistic framework to all three problems, that contains synthetic data, additional metrics for time series and dynamic time warping.

Scheer, Richard; Straßburger, Steffen; Knapp, Marc
Hybridization of the Digital Twin - overcoming implementation challenges. - In: Proceedings of the 56th Annual Hawaii International Conference on System Sciences, (2023), S. 1438-1447

In the context of Industry 4.0 the concept of the Digital Twin has gained significant momentum in industry as well as academia. Researchers have hypothesized a great number of potential benefits of the concept's usage. However, few real-world implementations have been recorded. This paper addresses the most pressing challenges inhibiting the concept's industrial application. It describes the process of the concept's hybridization to achieve a practical implementation strategy: the Hybrid Digital Twin. Subsequently, a prototype is implemented using a presently operational real-world manufacturing system to substantiate the viability of the methodology. Finally, the benefits, remaining issues and future developments of the concept are discussed.

Morlang, Frank; Straßburger, Steffen
On the role of HLA-based simulation in New Space. - In: 2022 Winter Simulation Conference (WSC), (2022), S. 430-440

This paper discusses High Level Architecture (HLA) based simulation in the context of the emergence of the private spaceflight industry called New Space. We postulate that distributed simulation plays a fundamental role in facilitating new opportunities of a cost efficient access to space. HLA defines a simulation system's architecture framework with a focus on reusability and interoperability. The article will therefore discuss the impact of its usage on the potential of affordable new aerospace systems developments. Future possibilities with an increased level of loose component coupling are presented.

Feldkamp, Niclas; Genath, Jonas; Straßburger, Steffen
Explainable AI for data farming output analysis: a use case for knowledge generation through black-box classifiers. - In: 2022 Winter Simulation Conference (WSC), (2022), S. 1152-1163

Data farming combines large-scale simulation experiments with high performance computing and sophisticated big data analysis methods. The portfolio of analysis methods for those large amounts of simulation data still yields potential to further development, and new methods emerge frequently. Among the most interesting are methods of explainable artificial intelligence (XAI). Those methods enable the use of black-box-classifiers for data farming output analysis, which has been shown in a previous paper. In this paper, we apply the concept for XAI-based data farming analysis on a complex, real world case study to investigate the suitability of such concept in a real world application, and we also elaborate on which black-box classifiers are actually the most suitable for large-scale simulation data that accumulates in a data farming project.

Wörrlein, Benjamin; Straßburger, Steffen
Hochaufgelöste Energieprofile durch hybride Simulation. - In: ASIM SST 2022 Proceedings Langbeiträge, (2022), S. 243-251

The price of a commodity, as electricity, is determined on a commodity market. A market is efficient when the supply and demand in the market are at an equilibrium. Efficient markets run on information. Information can cause a spontaneous and instantaneous change within the supply and demand in a market. The market communicates this new equilibrium through the change of the price of a commodity. In the electricity market the supplier and consumer communicate through electrical load profiles. A load profile signals when and how much energy should be consumed within a certain time frame without causing a change in the price of electricity. Creating such load profiles is commonly done by the supplier of energy by means of standard load profiles. Here we propose a data-driven simulation-based method that allows for the consumer to create its own specific load profile, which potentially will bring down the cost of energy consumed.

Feldkamp, Niclas; Bergmann, Sören; Straßburger, Steffen
Simulationsbasiertes Deep Reinforcement Learning für Modulare Produktionssysteme. - In: ASIM SST 2022 Proceedings Langbeiträge, (2022), S. 65-72

Modulare Produktionssysteme sollen die traditionelle Fließbandproduktion in der Automobilindustrie ablösen. Die Idee dabei ist, dass sich hochgradig individualisierte Produkte dynamisch und autonom durch ein System flexibler Arbeitsstationen bewegen können. Dieser Ansatz stellt hohe Anforderungen an die Planung und Organisation solcher Systeme. Da jedes Produkt seinen Weg durch das System frei und individuell bestimmen kann, kann die Implementierung von Regeln und Heuristiken, die die Flexibilität des Systems zur Leistungssteigerung ausnutzen, in diesem dynamischen Umfeld schwierig sein. Transportaufgaben werden in der Regel von fahrerlosen Transportsystemen (FTS) ausgeführt. Daher bietet die Integration von KI-basierten Steuerungslogiken eine vielversprechende Alternative zu manuell implementierten Entscheidungsregeln für den Betrieb der FTS. In diesem Beitrag wird ein Ansatz für den Einsatz von Reinforcement Learning (RL) in Kombination mit Simulation vorgestellt, um FTS in modularen Produktionssystemen zu steuern. Darüber hinaus werden Untersuchungen zu dessen Flexibilität und Skalierbarkeit durchgeführt.

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

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.