2019 |
Wolny, Sabine; Mazak, Alexandra; Wimmer, Manuel; Huemer, Christian Model-driven Runtime State Identification Inproceedings Proceedings of the Conference on Digital Ecosystems of the Future: Methods, Techniques and Applications (EMISA) - EMISA Forum, pp. 29-44, 2019. @inproceedings{Wolny2019mdrsi, title = {Model-driven Runtime State Identification}, author = {Sabine Wolny and Alexandra Mazak and Manuel Wimmer and Christian Huemer}, url = {https://cdl-mint.se.jku.at/wp-content/uploads/2020/04/EMISA_2019.pdf https://cdl-mint.se.jku.at/case-study-artefacts-for-emisa-2019/ }, year = {2019}, date = {2019-12-20}, booktitle = {Proceedings of the Conference on Digital Ecosystems of the Future: Methods, Techniques and Applications (EMISA) - EMISA Forum}, volume = {39}, number = {1}, pages = {29-44}, abstract = {With new advances such as Cyber-Physical Systems (CPS) and Internet of Things (IoT), more and more discrete software systems interact with continuous physical systems. State machines are a classical approach to specify the intended behavior of discrete systems during development. However, the actual realized behavior may deviate from those specified models due to environmental impacts, or measurement inaccuracies. Accordingly, data gathered at runtime should be validated against the specified model. A first step in this direction is to identify the individual system states of each execution of a system at runtime. This is a particular challenge for continuous systems where system states may be only identified by listening to sensor value streams. A further challenge is to raise these raw value streams on a model level for checking purposes. To tackle these challenges, we introduce a model-driven runtime state identification approach. In particular, we automatically derive corresponding time-series database queries from state machines in order to identify system runtime states based on the sensor value streams of running systems. We demonstrate our approach for a subset of SysML and evaluate it based on a case study of a simulated environment of a five-axes grip-arm robot within a working station.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } With new advances such as Cyber-Physical Systems (CPS) and Internet of Things (IoT), more and more discrete software systems interact with continuous physical systems. State machines are a classical approach to specify the intended behavior of discrete systems during development. However, the actual realized behavior may deviate from those specified models due to environmental impacts, or measurement inaccuracies. Accordingly, data gathered at runtime should be validated against the specified model. A first step in this direction is to identify the individual system states of each execution of a system at runtime. This is a particular challenge for continuous systems where system states may be only identified by listening to sensor value streams. A further challenge is to raise these raw value streams on a model level for checking purposes. To tackle these challenges, we introduce a model-driven runtime state identification approach. In particular, we automatically derive corresponding time-series database queries from state machines in order to identify system runtime states based on the sensor value streams of running systems. We demonstrate our approach for a subset of SysML and evaluate it based on a case study of a simulated environment of a five-axes grip-arm robot within a working station. |
Mazak, Alexandra; Wolny, Sabine; Wimmer, Manuel On the Need for Data-based Model-driven Engineering Incollection Biffl, Stefan; Eckhart, Matthias; Lüder, Arndt; Weippl, Edgar R (Ed.): Security and Quality in Cyber-Physical Systems Engineering, With Forewords by Robert M. Lee and Tom Gilb, pp. 103-127, Springer, 2019. @incollection{Mazak2019dbmde, title = {On the Need for Data-based Model-driven Engineering}, author = {Alexandra Mazak and Sabine Wolny and Manuel Wimmer }, editor = {Stefan Biffl and Matthias Eckhart and Arndt Lüder and Edgar R. Weippl}, doi = {10.1007/978-3-030-25312-7\_5}, year = {2019}, date = {2019-11-25}, booktitle = {Security and Quality in Cyber-Physical Systems Engineering, With Forewords by Robert M. Lee and Tom Gilb}, pages = {103-127}, publisher = {Springer}, chapter = {5}, abstract = {In order to deal with the increasing complexity of modern systems such as in software-intensive environments, models are used in many research fields as abstract descriptions of reality. On the one side, a model serves as an abstraction for a specific purpose, as a kind of “blueprint” of a system, describing a system’s structure and desired behavior in the design phase. On the other side, there are so-called runtime models providing real abstractions of systems during runtime, e.g., to monitor runtime behavior. Today, we recognize a discrepancy between the early snapshots and their real world correspondents. To overcome this discrepancy, we propose to fully integrate models from the very beginning within the lifecycle of a system. As a first step in this direction, we introduce a data-based model-driven engineering approach where we provide a unifying framework to combine downstream information from the model-driven engineering process with upstream information gathered during a system’s operation at runtime, by explicitly considering also a timing component. We present this temporal model framework step-by-step by selected use cases with increasing complexity.}, keywords = {}, pubstate = {published}, tppubtype = {incollection} } In order to deal with the increasing complexity of modern systems such as in software-intensive environments, models are used in many research fields as abstract descriptions of reality. On the one side, a model serves as an abstraction for a specific purpose, as a kind of “blueprint” of a system, describing a system’s structure and desired behavior in the design phase. On the other side, there are so-called runtime models providing real abstractions of systems during runtime, e.g., to monitor runtime behavior. Today, we recognize a discrepancy between the early snapshots and their real world correspondents. To overcome this discrepancy, we propose to fully integrate models from the very beginning within the lifecycle of a system. As a first step in this direction, we introduce a data-based model-driven engineering approach where we provide a unifying framework to combine downstream information from the model-driven engineering process with upstream information gathered during a system’s operation at runtime, by explicitly considering also a timing component. We present this temporal model framework step-by-step by selected use cases with increasing complexity. |
Wolny, Sabine; Mazak, Alexandra; Wimmer, Manuel Automatic Reverse Engineering of Interaction Models from System Logs Inproceedings Proceedings of the 24th IEEE Conference on Emerging Technologies and Factory Automation (ETFA), Zaragoza, Spain, September 10-13, 2019, pp. 57-64, IEEE, 2019. @inproceedings{Wolny2019reverse, title = {Automatic Reverse Engineering of Interaction Models from System Logs}, author = {Sabine Wolny and Alexandra Mazak and Manuel Wimmer}, url = {https://cdl-mint.se.jku.at/case-study-artefacts-for-etfa-2019/}, doi = {10.1109/ETFA.2019.8869502}, year = {2019}, date = {2019-10-24}, booktitle = {Proceedings of the 24th IEEE Conference on Emerging Technologies and Factory Automation (ETFA), Zaragoza, Spain, September 10-13, 2019}, pages = {57-64}, publisher = {IEEE}, abstract = {Nowadays, software- as well as hardware systems produce log files that enable a continuous monitoring of the system during its execution. Unfortunately, such text-based log traces are very long and difficult to read, and therefore, reasoning and analyzing runtime behavior is not straightforward. However, dealing with log traces is especially needed in cases, where (i) the execution of the system did not perform as intended, (ii) the process flow is unknown because there are no records, and/or (iii) the design models do not correspond to its realworld counterpart. These facts cause that log data has to be prepared in a more user-friendly way (e.g., in form of graphical representations) and it takes that algorithms are needed for automatically monitoring the system’s operation, and for tracking the system components interaction patterns. For this purpose we present an approach for transforming raw sensor data logs to a UML or SysML sequence diagram in order to provide a graphical representation for tracking log traces in a time-ordered manner. Based on this sequence diagram, we automatically identify interaction models in order to analyze the runtime behavior of system components. We implement this approach as prototypical plug-in in the modeling tool Enterprise Architect and evaluate it by an example of a self-driving car.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Nowadays, software- as well as hardware systems produce log files that enable a continuous monitoring of the system during its execution. Unfortunately, such text-based log traces are very long and difficult to read, and therefore, reasoning and analyzing runtime behavior is not straightforward. However, dealing with log traces is especially needed in cases, where (i) the execution of the system did not perform as intended, (ii) the process flow is unknown because there are no records, and/or (iii) the design models do not correspond to its realworld counterpart. These facts cause that log data has to be prepared in a more user-friendly way (e.g., in form of graphical representations) and it takes that algorithms are needed for automatically monitoring the system’s operation, and for tracking the system components interaction patterns. For this purpose we present an approach for transforming raw sensor data logs to a UML or SysML sequence diagram in order to provide a graphical representation for tracking log traces in a time-ordered manner. Based on this sequence diagram, we automatically identify interaction models in order to analyze the runtime behavior of system components. We implement this approach as prototypical plug-in in the modeling tool Enterprise Architect and evaluate it by an example of a self-driving car. |
Wurl, Alexander; Falkner, Andreas; Haselböck, Alois; Mazak, Alexandra; Filzmoser, Peter Exploring Robustness in a Combined Feature Selection Approach Inproceedings Hammoudi, Slimane; Quix, Christoph; Bernardino, Jorge (Ed.): Proceedings of the 8th International Conference on Data Science, Technology and Applications, DATA 2019, Prague, Czech Republic, July 26-28, 2019., pp. 84-91, SciTePress, 2019, ISBN: 978-989-758-377-3. @inproceedings{Wurl2019exploring, title = {Exploring Robustness in a Combined Feature Selection Approach}, author = {Alexander Wurl and Andreas Falkner and Alois Haselböck and Alexandra Mazak and Peter Filzmoser}, editor = {Slimane Hammoudi and Christoph Quix and Jorge Bernardino}, doi = {10.5220/0007924400840091}, isbn = {978-989-758-377-3}, year = {2019}, date = {2019-09-18}, booktitle = {Proceedings of the 8th International Conference on Data Science, Technology and Applications, DATA 2019, Prague, Czech Republic, July 26-28, 2019.}, pages = {84-91}, publisher = {SciTePress}, abstract = {A crucial task in the bidding phase of industrial systems is a precise prediction of the number of hardware components of specific types for the proposal of a future project. Linear regression models, trained on data of past projects, are efficient in supporting such decisions. The number of features used by these regression models should be as small as possible, so that determining their quantities generates minimal effort. The fact that training data are often ambiguous, incomplete, and contain outlier makes challenging demands on the robustness of the feature selection methods used. We present a combined feature selection approach: (i) iteratively learn a robust well-fitted statistical model and rule out irrelevant features, (ii) perform redundancy analysis to rule out dispensable features. In a case study from the domain of hardware management in Rail Automation we show that this approach assures robustness in the calculation of hardware components. Ist auch noch nicht online.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } A crucial task in the bidding phase of industrial systems is a precise prediction of the number of hardware components of specific types for the proposal of a future project. Linear regression models, trained on data of past projects, are efficient in supporting such decisions. The number of features used by these regression models should be as small as possible, so that determining their quantities generates minimal effort. The fact that training data are often ambiguous, incomplete, and contain outlier makes challenging demands on the robustness of the feature selection methods used. We present a combined feature selection approach: (i) iteratively learn a robust well-fitted statistical model and rule out irrelevant features, (ii) perform redundancy analysis to rule out dispensable features. In a case study from the domain of hardware management in Rail Automation we show that this approach assures robustness in the calculation of hardware components. Ist auch noch nicht online. |
Wurl, Alexander; Falkner, Andreas; Haselböck, Alois; Mazak, Alexandra A Conceptual Design of a Digital Companion for Failure Analysis in Rail Automation Inproceedings Becker, Jörg; Novikov, Dmitriy (Ed.): Proceedings of the 21st IEEE Conference on Business Informatics (CBI 2019), Moscow, Russia, July 15-17, 2019, Volume 1 - Research Papers, pp. 578–583, IEEE, 2019. @inproceedings{Wurl2019companion, title = {A Conceptual Design of a Digital Companion for Failure Analysis in Rail Automation}, author = {Alexander Wurl and Andreas Falkner and Alois Haselböck and Alexandra Mazak}, editor = {Jörg Becker and Dmitriy Novikov}, doi = {10.1109/CBI.2019.00073}, year = {2019}, date = {2019-08-27}, booktitle = {Proceedings of the 21st IEEE Conference on Business Informatics (CBI 2019), Moscow, Russia, July 15-17, 2019, Volume 1 - Research Papers}, pages = {578--583}, publisher = {IEEE}, abstract = {In Rail Automation, a crucial task in the maintenance phase comprises the process of failure analysis. Domain experts are often faced with various challenges in analyzing large data volumes which reveal highly complex data structures. However, finding causes for potential failures and deciding how to optimize or repair the system may be extensively time consuming. To this end, we propose the concept of a digital companion which serves as continuous assistant recommending optimizations. A sequence of different data analytics methods within the digital companion enables the domain expert to reasonably manage and control the process of failure analysis. In illustrative examples, we give insights in the workflow of a digital companion and discuss the application in the domain of Rail Automation.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } In Rail Automation, a crucial task in the maintenance phase comprises the process of failure analysis. Domain experts are often faced with various challenges in analyzing large data volumes which reveal highly complex data structures. However, finding causes for potential failures and deciding how to optimize or repair the system may be extensively time consuming. To this end, we propose the concept of a digital companion which serves as continuous assistant recommending optimizations. A sequence of different data analytics methods within the digital companion enables the domain expert to reasonably manage and control the process of failure analysis. In illustrative examples, we give insights in the workflow of a digital companion and discuss the application in the domain of Rail Automation. |
2018 |
Wolny, Sabine; Mazak, Alexandra; Wally, Bernhard An Initial Mapping Study on MDE4IoT Inproceedings Proceedings of the 2nd International Workshop on Model-Driven Engineering for the Internet-of-Things (MDE4IoT 2018), 2018. @inproceedings{Wolny2018mde4iot, title = {An Initial Mapping Study on MDE4IoT}, author = {Sabine Wolny and Alexandra Mazak and Bernhard Wally}, year = {2018}, date = {2018-10-24}, booktitle = {Proceedings of the 2nd International Workshop on Model-Driven Engineering for the Internet-of-Things (MDE4IoT 2018)}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Mazak, Alexandra; Wimmer, Manuel; Patsuk-Boesch, Polina Execution-based Model Profiling Inproceedings Post-Proceedings of the6th International Symposium on Data-Driven Process Discovery and Analysis, pp. 37-52, Springer International Publishing, Cham, 2018, ISBN: 978-3-319-74160-4. @inproceedings{Mazak2017ebmp, title = {Execution-based Model Profiling}, author = {Alexandra Mazak and Manuel Wimmer and Polina Patsuk-Boesch}, doi = {10.1007/978-3-319-74161-1_3}, isbn = {978-3-319-74160-4}, year = {2018}, date = {2018-01-26}, booktitle = {Post-Proceedings of the6th International Symposium on Data-Driven Process Discovery and Analysis}, volume = {307}, pages = {37-52}, publisher = {Springer International Publishing}, address = {Cham}, series = {Lecture Notes in Business Information Processing}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
2017 |
Wolny, Sabine; Mazak, Alexandra; Konlechner, Rafael; Wimmer, Manuel Towards Continuous Behavior Mining Inproceedings Ceravolo, P; Keulen, Van M; Stoffel, K (Ed.): CEUR Workshop Proceedings: Proceedings of the 7th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2017), pp. 149-150, 2017, ISSN: 1613-0073. @inproceedings{Wolny2017, title = {Towards Continuous Behavior Mining}, author = {Sabine Wolny and Alexandra Mazak and Rafael Konlechner and Manuel Wimmer}, editor = {P. Ceravolo and M. Van Keulen and K. Stoffel}, url = {http://ceur-ws.org/Vol-2016/paper13.pdf}, issn = {1613-0073}, year = {2017}, date = {2017-12-07}, booktitle = {CEUR Workshop Proceedings: Proceedings of the 7th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2017)}, volume = {Vol-2016}, pages = {149-150}, abstract = {With new advances in Cyber-Physical Systems (CPS) and Internet of Things (IoT), more and more discrete software controllers interact with continuous physical systems. Workflow models are a classical approach to define controllers. However, the effect of the associated actions that are activated by executing the workflow may not spontaneously be realized but have to be realized over time. Generally, behavioral model elements such as activities in workflow languages are displayed mostly as black box, meaning that it is not possible to trace variable changes over time in most of the classical modeling approaches. In this paper, we introduce an envisioned architecture to cope with this challenge.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } With new advances in Cyber-Physical Systems (CPS) and Internet of Things (IoT), more and more discrete software controllers interact with continuous physical systems. Workflow models are a classical approach to define controllers. However, the effect of the associated actions that are activated by executing the workflow may not spontaneously be realized but have to be realized over time. Generally, behavioral model elements such as activities in workflow languages are displayed mostly as black box, meaning that it is not possible to trace variable changes over time in most of the classical modeling approaches. In this paper, we introduce an envisioned architecture to cope with this challenge. |
Wally, Bernhard; Huemer, Christian; Mazak, Alexandra Aligning Business Services with Production Services: The Case of REA and ISA-95 Inproceedings Proceedings of the 10th IEEE International Conference on Service Oriented Computing and Applications (SOCA 2017), 2017. @inproceedings{Wally2017abs, title = {Aligning Business Services with Production Services: The Case of REA and ISA-95}, author = {Bernhard Wally and Christian Huemer and Alexandra Mazak}, doi = {10.1109/SOCA.2017.10}, year = {2017}, date = {2017-11-22}, booktitle = {Proceedings of the 10th IEEE International Conference on Service Oriented Computing and Applications (SOCA 2017)}, abstract = {"Industrie 4.0" aims at flexible production networks that require horizontal integration across companies. Evidently, any production related information exchanged in the network must be vertically forwarded to the corresponding service endpoints of the local production system. Accordingly, there is a need to align information that flows between companies and within each company. The Resource-Event-Agent (REA) business ontology describes a metamodel for internal business activities (e.g., production) and for inter-organizational exchange constellations on the enterprise resource planning (ERP) level. ISA-95 is a series of standards targeting the integration of enterprise control systems on the interface between ERP systems and manufacturing execution systems. Consequently, we align elements of REA and ISA-95 and define conversion rules for the transformation of elements from one system to the other. By interleaving the semantics of both standards, we formally strengthen the links between the services of the business level and the production level, and support multi-system adaptation in flexible production environments.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } "Industrie 4.0" aims at flexible production networks that require horizontal integration across companies. Evidently, any production related information exchanged in the network must be vertically forwarded to the corresponding service endpoints of the local production system. Accordingly, there is a need to align information that flows between companies and within each company. The Resource-Event-Agent (REA) business ontology describes a metamodel for internal business activities (e.g., production) and for inter-organizational exchange constellations on the enterprise resource planning (ERP) level. ISA-95 is a series of standards targeting the integration of enterprise control systems on the interface between ERP systems and manufacturing execution systems. Consequently, we align elements of REA and ISA-95 and define conversion rules for the transformation of elements from one system to the other. By interleaving the semantics of both standards, we formally strengthen the links between the services of the business level and the production level, and support multi-system adaptation in flexible production environments. |
Mazak, Alexandra; Wimmer, Manuel Sequence Pattern Mining: Automatisches Erkennen und Auswerten von Interaktionsmustern zwischen technischen Assets basierend auf SysML-Sequenzdiagrammen Inproceedings Proceedings of Tag des Software Engineerings (TdSE 2017), Paderborn, 2017. @inproceedings{Mazak2017tdse, title = {Sequence Pattern Mining: Automatisches Erkennen und Auswerten von Interaktionsmustern zwischen technischen Assets basierend auf SysML-Sequenzdiagrammen}, author = {Alexandra Mazak and Manuel Wimmer}, year = {2017}, date = {2017-11-09}, booktitle = {Proceedings of Tag des Software Engineerings (TdSE 2017)}, address = {Paderborn}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Wimmer, Manuel; Novak, Petr; Sindelar, Radek; Berardinelli, Luca; Mayerhofer, Tanja; Mazak, Alexandra Cardinality-Based Variability Modeling with AutomationML Inproceedings Proceedings of the 22nd IEEE International Conference on Emerging Technology & Factory Automation (ETFA 2017), 2017. @inproceedings{Wimmer2017cbvm, title = {Cardinality-Based Variability Modeling with AutomationML}, author = {Manuel Wimmer and Petr Novak and Radek Sindelar and Luca Berardinelli and Tanja Mayerhofer and Alexandra Mazak}, year = {2017}, date = {2017-09-13}, booktitle = {Proceedings of the 22nd IEEE International Conference on Emerging Technology & Factory Automation (ETFA 2017)}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Wally, Bernhard; Huemer, Christian; Mazak, Alexandra A View on Model-Driven Vertical Integration: Alignment of Production Facility Models and Business Models Inproceedings Proceedings of the 13th IEEE Conference on Automation Science and Engineering (CASE 2017), 2017. @inproceedings{Wally2017mdvi, title = {A View on Model-Driven Vertical Integration: Alignment of Production Facility Models and Business Models}, author = {Bernhard Wally and Christian Huemer and Alexandra Mazak}, doi = {10.1109/COASE.2017.8256235}, year = {2017}, date = {2017-08-20}, booktitle = {Proceedings of the 13th IEEE Conference on Automation Science and Engineering (CASE 2017)}, abstract = {Smart manufacturing requires deeply integrated IT systems in order to foster flexibility in the setup, re-arrangement and use of attached manufacturing systems. In a vertical integration scenario, IT systems of different vendors might be in use and proprietary interfaces need to defined in order to allow the exchange of relevant information from one system to another. In this paper we present a model-driven approach for vertical integration of IT systems. It is based on the application of industry standards for the representation of hierarchy level specific system properties and an alignment of their key concepts in order to provide bridging functions for the transformation between the different systems.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Smart manufacturing requires deeply integrated IT systems in order to foster flexibility in the setup, re-arrangement and use of attached manufacturing systems. In a vertical integration scenario, IT systems of different vendors might be in use and proprietary interfaces need to defined in order to allow the exchange of relevant information from one system to another. In this paper we present a model-driven approach for vertical integration of IT systems. It is based on the application of industry standards for the representation of hierarchy level specific system properties and an alignment of their key concepts in order to provide bridging functions for the transformation between the different systems. |
Mazak, Alexandra; Wimmer, Manuel; Patsuk-Boesch, Polina Reverse Engineering of Production Processes based on Markov Chains Inproceedings Proceedings of the 13th IEEE Conference on Automation Science and Engineering (CASE 2017), 2017. @inproceedings{Mazak2017repp, title = {Reverse Engineering of Production Processes based on Markov Chains}, author = {Alexandra Mazak and Manuel Wimmer and Polina Patsuk-Boesch}, year = {2017}, date = {2017-08-20}, booktitle = {Proceedings of the 13th IEEE Conference on Automation Science and Engineering (CASE 2017)}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Wurl, Alexander; Falkner, Andreas; Haselböck, Alois; Mazak, Alexandra Using Signifiers for Data Integration in Rail Automation Inproceedings Proceedings of the 6th International Conference on Data Science, Technology and Applications (DATA 2017), 2017. @inproceedings{Wurl2017usdi, title = {Using Signifiers for Data Integration in Rail Automation}, author = {Alexander Wurl and Andreas Falkner and Alois Haselböck and Alexandra Mazak}, year = {2017}, date = {2017-07-24}, booktitle = {Proceedings of the 6th International Conference on Data Science, Technology and Applications (DATA 2017)}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Bill, Robert; Mazak, Alexandra; Wimmer, Manuel; Vogel-Heuser, Birgit On the Need for Temporal Model Repositories Inproceedings Proceedings of the 1st International Workshop of Grand Challenges in Modeling (GRAND 2017), 2017. @inproceedings{Bill2017tmr, title = {On the Need for Temporal Model Repositories}, author = {Robert Bill and Alexandra Mazak and Manuel Wimmer and Birgit Vogel-Heuser}, year = {2017}, date = {2017-07-17}, booktitle = {Proceedings of the 1st International Workshop of Grand Challenges in Modeling (GRAND 2017)}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Artner, Johannes; Mazak, Alexandra; Wimmer, Manuel Towards Stochastic Performance Models for Web 2.0 Applications Inproceedings Proceedings of the 17th International Conference on Web Engineering (ICWE 2017), pp. 360-369, 2017. @inproceedings{Artner2017tspm, title = {Towards Stochastic Performance Models for Web 2.0 Applications}, author = {Johannes Artner and Alexandra Mazak and Manuel Wimmer}, year = {2017}, date = {2017-06-01}, booktitle = {Proceedings of the 17th International Conference on Web Engineering (ICWE 2017)}, pages = {360-369}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
Berardinelli, Luca; Mazak, Alexandra; Alt, Oliver; Wimmer, Manuel; Kappel, Gerti Model-Driven Systems Engineering: Principles and Applications in the CPPS Domain Book Chapter Biffl, Stefan; Lueder, Arndt; Gerhard, Detlef (Ed.): Multi-Disciplinary Engineering for Cyber-Physical Production Systems: Data Models and Software Solutions for Handling Complex Engineering Projects, pp. 261-299, Springer International Publishing, 2017, ISBN: 978-3-319-56345-9. @inbook{Berardinelli2017mdse, title = {Model-Driven Systems Engineering: Principles and Applications in the CPPS Domain}, author = {Luca Berardinelli and Alexandra Mazak and Oliver Alt and Manuel Wimmer and Gerti Kappel}, editor = {Stefan Biffl and Arndt Lueder and Detlef Gerhard}, doi = {10.1007/978-3-319-56345-9_11}, isbn = {978-3-319-56345-9}, year = {2017}, date = {2017-05-07}, booktitle = {Multi-Disciplinary Engineering for Cyber-Physical Production Systems: Data Models and Software Solutions for Handling Complex Engineering Projects}, pages = {261-299}, publisher = {Springer International Publishing}, abstract = {To engineer large, complex, and interdisciplinary systems, modeling is considered as the universal technique to understand and simplify reality through abstraction, and thus, models are in the center as the most important artifacts throughout interdisciplinary activities within model-driven engineering processes. Model-Driven Systems Engineering (MDSE) is a systems engineering paradigm that promotes the systematic adoption of models throughout the engineering process by identifying and integrating appropriate concepts, languages, techniques, and tools. This chapter discusses current advances as well as challenges towards the adoption of model-driven approaches in cyber-physical production systems (CPPS) engineering. In particular, we discuss how modeling standards, modeling languages, and model transformations are employed to support current systems engineering processes in the CPPS domain, and we show their integration and application based on a case study concerning a lab-sized production system. The major outcome of this case study is the realization of an automated engineering tool chain, including the languages SysML, AML, and PMIF, to perform early design and validation.}, keywords = {}, pubstate = {published}, tppubtype = {inbook} } To engineer large, complex, and interdisciplinary systems, modeling is considered as the universal technique to understand and simplify reality through abstraction, and thus, models are in the center as the most important artifacts throughout interdisciplinary activities within model-driven engineering processes. Model-Driven Systems Engineering (MDSE) is a systems engineering paradigm that promotes the systematic adoption of models throughout the engineering process by identifying and integrating appropriate concepts, languages, techniques, and tools. This chapter discusses current advances as well as challenges towards the adoption of model-driven approaches in cyber-physical production systems (CPPS) engineering. In particular, we discuss how modeling standards, modeling languages, and model transformations are employed to support current systems engineering processes in the CPPS domain, and we show their integration and application based on a case study concerning a lab-sized production system. The major outcome of this case study is the realization of an automated engineering tool chain, including the languages SysML, AML, and PMIF, to perform early design and validation. |
Wally, Bernhard; Huemer, Christian; Mazak, Alexandra Entwining Plant Engineering Data and ERP Information: Vertical Integration with AutomationML and ISA-95 Inproceedings Proceedings of the 3rd IEEE International Conference on Control, Automation and Robotics (ICCAR 2017), pp. 356-364, 2017, ISBN: 978-1-5090-6089-4. @inproceedings{Wally2017viai, title = {Entwining Plant Engineering Data and ERP Information: Vertical Integration with AutomationML and ISA-95}, author = {Bernhard Wally and Christian Huemer and Alexandra Mazak}, doi = {10.1109/ICCAR.2017.7942718}, isbn = {978-1-5090-6089-4}, year = {2017}, date = {2017-04-26}, booktitle = {Proceedings of the 3rd IEEE International Conference on Control, Automation and Robotics (ICCAR 2017)}, pages = {356-364}, abstract = {IT systems' integration in manufacturing companies is currently investigated in both academia and industry. While there can be found specialized systems and standards that tackle specific, e.g., production relevant problems, little has been done in the alignment of and transformation between such industrial standards. We will present the alignment of two specialized international standards, which will foster vertical system integration through detailed mapping of related concepts: (i) the Automation Markup Language (AML) standardizes the modeling of factory shop floors on top of the XML-based Computer Aided Engineering Exchange (CAEX) data format and (ii) ISA-95 is a series of standards targeting the integration of enterprise control systems, most prominent enterprise resource planning systems and manufacturing execution systems. In order to provide higher level semantics to lower level system descriptions, we have (i) aligned elements from AML and ISA-95 in order to make explicit both overlaps and complementary concepts and (ii) defined a ruleset for referencing external ISA-95 documents/elements from AML documents. Finally, we have developed a scenario that shows the potential use case for such an entwined use of AML and ISA-95.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } IT systems' integration in manufacturing companies is currently investigated in both academia and industry. While there can be found specialized systems and standards that tackle specific, e.g., production relevant problems, little has been done in the alignment of and transformation between such industrial standards. We will present the alignment of two specialized international standards, which will foster vertical system integration through detailed mapping of related concepts: (i) the Automation Markup Language (AML) standardizes the modeling of factory shop floors on top of the XML-based Computer Aided Engineering Exchange (CAEX) data format and (ii) ISA-95 is a series of standards targeting the integration of enterprise control systems, most prominent enterprise resource planning systems and manufacturing execution systems. In order to provide higher level semantics to lower level system descriptions, we have (i) aligned elements from AML and ISA-95 in order to make explicit both overlaps and complementary concepts and (ii) defined a ruleset for referencing external ISA-95 documents/elements from AML documents. Finally, we have developed a scenario that shows the potential use case for such an entwined use of AML and ISA-95. |
Wally, Bernhard; Huemer, Christian; Mazak, Alexandra ISA-95 based Task Specification Layer for REA in Production Environments Inproceedings Proceedings of the 11th International Workshop on Value Modeling and Business Ontologies (VMBO 2017), 2017. @inproceedings{Wally2017tsl, title = {ISA-95 based Task Specification Layer for REA in Production Environments}, author = {Bernhard Wally and Christian Huemer and Alexandra Mazak}, year = {2017}, date = {2017-03-07}, booktitle = {Proceedings of the 11th International Workshop on Value Modeling and Business Ontologies (VMBO 2017)}, abstract = {Resource-Event-Agent (REA) has been applied to various engineering and business domains, with a focus on transfer activities rather than transformation activities. In the context of smart manufacturing, vertical integration of IT systems (e.g., business applications and production control systems) is a key factor. In this work, we shed light on the integration of REA concepts into production environments by investigating properties of REA transformations and aligning them with concepts from an international standard for enterprise-control system integration (ISA-95).}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Resource-Event-Agent (REA) has been applied to various engineering and business domains, with a focus on transfer activities rather than transformation activities. In the context of smart manufacturing, vertical integration of IT systems (e.g., business applications and production control systems) is a key factor. In this work, we shed light on the integration of REA concepts into production environments by investigating properties of REA transformations and aligning them with concepts from an international standard for enterprise-control system integration (ISA-95). |
2019 |
Model-driven Runtime State Identification Inproceedings Proceedings of the Conference on Digital Ecosystems of the Future: Methods, Techniques and Applications (EMISA) - EMISA Forum, pp. 29-44, 2019. |
On the Need for Data-based Model-driven Engineering Incollection Biffl, Stefan; Eckhart, Matthias; Lüder, Arndt; Weippl, Edgar R (Ed.): Security and Quality in Cyber-Physical Systems Engineering, With Forewords by Robert M. Lee and Tom Gilb, pp. 103-127, Springer, 2019. |
Automatic Reverse Engineering of Interaction Models from System Logs Inproceedings Proceedings of the 24th IEEE Conference on Emerging Technologies and Factory Automation (ETFA), Zaragoza, Spain, September 10-13, 2019, pp. 57-64, IEEE, 2019. |
Exploring Robustness in a Combined Feature Selection Approach Inproceedings Hammoudi, Slimane; Quix, Christoph; Bernardino, Jorge (Ed.): Proceedings of the 8th International Conference on Data Science, Technology and Applications, DATA 2019, Prague, Czech Republic, July 26-28, 2019., pp. 84-91, SciTePress, 2019, ISBN: 978-989-758-377-3. |
A Conceptual Design of a Digital Companion for Failure Analysis in Rail Automation Inproceedings Becker, Jörg; Novikov, Dmitriy (Ed.): Proceedings of the 21st IEEE Conference on Business Informatics (CBI 2019), Moscow, Russia, July 15-17, 2019, Volume 1 - Research Papers, pp. 578–583, IEEE, 2019. |
2018 |
An Initial Mapping Study on MDE4IoT Inproceedings Proceedings of the 2nd International Workshop on Model-Driven Engineering for the Internet-of-Things (MDE4IoT 2018), 2018. |
Execution-based Model Profiling Inproceedings Post-Proceedings of the6th International Symposium on Data-Driven Process Discovery and Analysis, pp. 37-52, Springer International Publishing, Cham, 2018, ISBN: 978-3-319-74160-4. |
2017 |
Towards Continuous Behavior Mining Inproceedings Ceravolo, P; Keulen, Van M; Stoffel, K (Ed.): CEUR Workshop Proceedings: Proceedings of the 7th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2017), pp. 149-150, 2017, ISSN: 1613-0073. |
Aligning Business Services with Production Services: The Case of REA and ISA-95 Inproceedings Proceedings of the 10th IEEE International Conference on Service Oriented Computing and Applications (SOCA 2017), 2017. |
Sequence Pattern Mining: Automatisches Erkennen und Auswerten von Interaktionsmustern zwischen technischen Assets basierend auf SysML-Sequenzdiagrammen Inproceedings Proceedings of Tag des Software Engineerings (TdSE 2017), Paderborn, 2017. |
Cardinality-Based Variability Modeling with AutomationML Inproceedings Proceedings of the 22nd IEEE International Conference on Emerging Technology & Factory Automation (ETFA 2017), 2017. |
A View on Model-Driven Vertical Integration: Alignment of Production Facility Models and Business Models Inproceedings Proceedings of the 13th IEEE Conference on Automation Science and Engineering (CASE 2017), 2017. |
Reverse Engineering of Production Processes based on Markov Chains Inproceedings Proceedings of the 13th IEEE Conference on Automation Science and Engineering (CASE 2017), 2017. |
Using Signifiers for Data Integration in Rail Automation Inproceedings Proceedings of the 6th International Conference on Data Science, Technology and Applications (DATA 2017), 2017. |
On the Need for Temporal Model Repositories Inproceedings Proceedings of the 1st International Workshop of Grand Challenges in Modeling (GRAND 2017), 2017. |
Towards Stochastic Performance Models for Web 2.0 Applications Inproceedings Proceedings of the 17th International Conference on Web Engineering (ICWE 2017), pp. 360-369, 2017. |
Model-Driven Systems Engineering: Principles and Applications in the CPPS Domain Book Chapter Biffl, Stefan; Lueder, Arndt; Gerhard, Detlef (Ed.): Multi-Disciplinary Engineering for Cyber-Physical Production Systems: Data Models and Software Solutions for Handling Complex Engineering Projects, pp. 261-299, Springer International Publishing, 2017, ISBN: 978-3-319-56345-9. |
Entwining Plant Engineering Data and ERP Information: Vertical Integration with AutomationML and ISA-95 Inproceedings Proceedings of the 3rd IEEE International Conference on Control, Automation and Robotics (ICCAR 2017), pp. 356-364, 2017, ISBN: 978-1-5090-6089-4. |
ISA-95 based Task Specification Layer for REA in Production Environments Inproceedings Proceedings of the 11th International Workshop on Value Modeling and Business Ontologies (VMBO 2017), 2017. |