Der Große Beleg dokumentiert die Entwicklung eines Java Code-Generators für Dresden OCL2 for Eclipse. Schwerpunkt der Arbeit liegt dabei auf der Abbildung der Object Constraint Language auf die Programmiersprache Java mit Hilfe von AspectJ.

Der Große Beleg dokumentiert die Entwicklung eines Java Code-Generators für Dresden OCL2 for Eclipse. Schwerpunkt der Arbeit liegt dabei auf der Abbildung der Object Constraint Language auf die Programmiersprache Java mit Hilfe von AspectJ.

Diese Arbeit stellt einen Prozess vor, der das Ziel hat, automatisch Kursmaterialen aus Wissen zu generieren und zu einem Kurs zusammenzusetzen. Dazu werden vier Phasen durchlaufen. Im ersten Prozess wird das Wissen gesammelt und strukturiert. Das gewünschte Material für einen Kurs wird in der zweiten Phase aus dem vorhanden Wissen generiert und in der dritten Phase zu einem Kurs zusammengesetzt. Die letzte Phase kann über die Zeit neuen Wissen erlangen und die Kursmaterialien aktualisieren. Dieser Prozess wird als Model-Driven Teaching (kurz MDTea) eingeführt. Bisher ist dieser Begriff nicht definiert wurden. Diese Arbeit erklärt die einzelnen Phasen von MDTea, zeigt verwandte Arbeiten zu dem Thema 'Generirung von Kursmaterialen/Kursen' und zeigt einen ersten Prototypen, welcher die zweite und dritte Phasen teilweise als 'Proof of concept' umsetzt.:1. Einleitung 1.1. Motivation 1.2. Forschungsfragen 2. Grundlagen 2.1. E-Learning und Lernplattformen 2.2. Model-Driven Development 2.3. Wissensgenerierung 3. Model-Driven Teaching 3.1. Architektur 3.2. Definierte Begriffe 3.3. Aggregation 3.3.1. Benötigte Materialien 3.3.2. Ablauf 3.3.3. Erzeugte Materialien 3.4. Generation 3.4.1. Benötigte Materialien 3.4.2. Ablauf 3.4.3. Materialgruppen 3.4.4. Erzeugte Materialien 3.5. Finalization 3.5.1. Benötigte Materialien 3.5.2. Ablauf 3.5.3. Erzeugte Materialien 3.6. Synchronization 3.6.1. Benötigte Materialien 3.6.2. Ablauf 3.6.3. Erzeugte Materialien 4. Verwandte Arbeiten 4.1. Phasen von MDTea 4.1.1. Arbeiten zu Aggregation 4.1.2. Arbeiten zur Generation 4.1.3. Arbeiten zur Finalization 4.1.4. Arbeiten zur Synchronization 4.2. Art der Umsetzung 4.2.1. Modell-getrieben 4.2.2. Modell-basiert 4.2.3. Anderer Ansatz 4.3. Weitere Konzepte aus der Literatur 4.4. Zusammenfassung 5. Design 5.1. Wissensmodell 5.1.1. Struktur 5.1.2. Elemente und Relationen 5.1.3. Quellen 5.2. Der Kursplan 5.2.1. Allgemeine Struktur 5.2.2. Metadaten 5.2.3. Inhalt 5.2.4. Struktur 5.3. Generator 5.4. Vorlagen 5.5. Synchronisationsinformationen 6. Implementierung 6.1. Das Wissensmodell 6.1.1. Datenhaltung 6.1.2. Wissensmodellgenerator 6.2. Der Kursplan 6.2.1. Datenhaltung 6.2.2. Der Lehrplangenerator 6.3. Das Tool - MDTea-Gen 6.3.1. Generation mit MDTea-Gen 6.3.2. Finalization mit MDTea-Gen 7. Auswertung 7.1. Testumgebung 7.1.1. Befüllung des Wissensmodell 7.1.2. Erstellung des Testkurses 7.2. Möglichkeiten und Limitationen des Prototypen 7.3. Möglichkeiten und Limitationen des generierten Kurses 7.4. Auswertung einer Fallstudie 7.4.1. Versuchsaufbau 7.4.2. Auswertung von Probanden 7.5. Zusammenfassung 8. Ausblick und Zusammenfassung 8.1. Beantwortung der Forschungsfragen 8.2. Ausblick 8.3. Zusammenfassung Literatur A. Bilder B. Listen 69 B.1. Antworten zu Frage 9 B.2. Antworten zu Frage 10 C. Weiters 71 C.1. Weitere Kommentare von P2 C.2. Fragebogen

The primary objective of wastewater treatment is the removal of pollutants to meet given legal effluent standards. To further reduce operators costs additional recovery of resources and energy is desired by industrial and municipal wastewater treatment. Hence the objective in early stage of planning of treatment facilities lies in the identification and evaluation of promising configurations of treatment units. Obviously this early stage of planning may best be supported by software tools to be able to deal with a variety of different treatment configurations. In chemical process engineering various design tools are available that automatically identify feasible process configurations for the purpose to obtain desired products from given educts. In contrast, the adaptation of these design tools for the automatic generation of treatment unit configurations (process chains) to achieve preset effluent standards is hampered by the following three reasons. First, pollutants in wastewater are usually not defined as chemical substances but by compound parameters according to equal properties (e.g. all particulate matter). Consequently the variation of a single compound parameter leads to a change of related parameters (e.g. relation between Chemical Oxygen Demand and Total Suspended Solids). Furthermore, mathematical process models of treatment processes are tailored towards fractions of compound parameters. This hampers the generic representation of these process models which in turn is essential for automatic identification of treatment configurations. Second, treatment technologies for wastewater treatment rely on a variety of chemical, biological, and physical phenomena. Approaches to mathematically describe these phenomena cover a wide range of modeling techniques including stochastic, conceptual or deterministic approaches. Even more the consideration of temporal and spatial resolutions differ. This again hampers a generic representation of process models. Third, the automatic identification of treatment configurations may either be achieved by the use of design rules or by permutation of all possible combinations of units stored within a database of treatment units. The first approach depends on past experience translated into design rules. Hence, no innovative new treatment configurations can be identified. The second approach to identify all possible configurations collapses by extremely high numbers of treatment configurations that cannot be mastered. This is due to the phenomena of combinatorial explosion. It follows therefrom that an appropriate planning algorithm should function without the need of additional design rules and should be able to identify directly feasible configurations while discarding those impractical. This work presents a planning tool for the identification and evaluation of treatment configurations that tackles the before addressed problems. The planning tool comprises two major parts. An external declarative knowledge base and the actual planning tool that includes a goal oriented planning algorithm. The knowledge base describes parameters for wastewater characterization (i.e. material model) and a set of treatment units represented by process models (i.e. process model). The formalization of the knowledge base is achieved by the Web Ontology Language (OWL). The developed data model being the organization structure of the knowledge base describes relations between wastewater parameters and process models to enable for generic representation of process models. Through these parameters for wastewater characterization as well as treatment units can be altered or added to the knowledge base without the requirement to synchronize already included parameter representations or process models. Furthermore the knowledge base describes relations between parameters and properties of water constituents. This allows to track changes of all wastewater parameters which result from modeling of removal efficiency of applied treatment units. So far two generic treatment units have been represented within the knowledge base. These are separation and conversion units. These two raw types have been applied to represent different types of clarifiers and biological treatment units. The developed planning algorithm is based on a Means-Ends Analysis (MEA). This is a goal oriented search algorithm that posts goals from wastewater state and limit value restrictions to select those treatment units only that are likely to solve the treatment problem. Regarding this, all treatment units are qualified according to postconditions that describe the effect of each unit. In addition, units are also characterized by preconditions that state the application range of each unit. The developed planning algorithm furthermore allows for the identification of simple cycles to account for moving bed reactor systems (e.g. functional unit of aeration tank and clarifier). The evaluation of identified treatment configurations is achieved by total estimated cost of each configuration. The planning tool has been tested on five use cases. Some use cases contained multiple sources and sinks. This showed the possibility to identify water reuse capabilities as well as to identify solutions that go beyond end of pipe solutions. Beyond the originated area of application, the planning tool may be used for advanced interrogations. Thereby the knowledge base and planning algorithm may be further developed to address the objectives to identify configurations for any type of material and energy recovery.

Microtubules are a part of the cell cytoskeleton that performs different functions, such as providing the mechanical support for the shape of a cell, acting as tracks along which the motor protein move organelles from one part of the cell to another, or the forming mitotic spindle during the cell division. The microtubules are dynamic structures, namely they can grow and shrink. The phase of microtubule growth alternates with the phase of shrinkage that results in the dynamic microtubule network in the cell. However, to form stable and spatially well-defined structures, such as a mitotic spindle, the cell needs to control this stochastic process. This is done by microtubule-associated proteins (MAPs). One class of MAPs is the proteins of XMAP216/Dis1 family, which are microtubule polymerases. The founding member of this family is X. laevis XMAP215. XMAP215 is a processive polymerase acting on the microtubule plus end. XMAP215 binds either directly or reaches the microtubule plus end by the diffusion along the microtubule lattice. Being at the microtubule plus-end XMAP215 stays there transiently and helps to incorporate up to 25 tubulin dimers into microtubule lattice before it dissociates and, therefore, it processively tracks the growing microtubule end during polymerization. There are two hypothesis of microtubule assembly promotion: (i) XMAP215 repeatedly releases an associated tubulin dimer into the microtubule growing plus end or (ii) structurally stabilizes a polymerized tubulin intermediate at the growing plus end and, therefore, preventing depolymerization events. The first way results into the increase of on-rate of tubulin dimers at the microtubule end, whereas the second way results into the decrease of off-rate of tubulin dimers at the microtubule end. Here, I show the study of the mechanism of microtubule growth acceleration by XMAP215 and the dependence of XMAP215 polymerization activity on the applied force. To answer these questions, I investigated the addition of tubulin dimers to the plus end of the microtubule by XMAP215 and how this addition depends on the applied force. XMAP215 remains at the microtubule end for several rounds of tubulin addition surfing both growing and shrinking microtubule ends. Therefore, if one could track the position of the XMAP215 molecules at the very tip of a microtubule with sufficient resolution, it would provide the information about the dynamics of the microtubule end. The technique, which can detect the position of the object of interest with high spatial and temporal resolution in addition to being able to exert a force, is an optical trap. A calibrated optical trap not only provides a good measure of displacement but also enables force measurements. To monitor the position of the molecules of interest, the molecules of interest are usually attached to a microsphere. Hence, I tethered XMAP215 to a microsphere held by an optical trap, and used XMAP215 as a handle to interact with the microtubule tip. When the microtubule grows, the XMAP215 coated microsphere will move in the optical trap and this movement can be detected with high temporal and spatial resolution. My work demonstrates that cooperatively working XMAP215 molecules can not only polymerize microtubule but also harness the energy of microtubule polymerization or depolymerization to transport some cargo. There is an evidence that orthologues of XMAP215 in budding yeasts, fission yeasts and Drosophila localize on the kinetochores. Therefore, the ability of the bearing some load during microtubule polymerization could be potentially important for the XMAP215 functioning during cell division. I also showed the influence of external force applied to the XMAP215 molecules. Pointing toward microtubule growth, a force of 0.5 pN applied to the microtubule tip-coupled XMAP215-coated microsphere increases XMAP215 polymerization activity. However, the force of the same magnitude but applied against microtubule growth does not affect XMAP215 polymerization activity. This result can be explained by the fact, that the force acting in the direction of microtubule growth constrains XMAP215 to be at the very microtubule tip. Hence, XMAP215 can not diffuse away from plus-end and there is higher chance to incorporate tubulin dimers into the microtubule plus-end. The on- and off-rate of tubulin dimers at the microtubule end are both decreased when the external force applied either in direction of microtubule growth or opposite to it. The external force affects the off-rate slightly stronger than on-rate of tubulin dimer. Taking together, my study gives new insights into the mechanism of microtubule polymerization by XMAP215 and shows some novel properties of this protein.

The primary objective of wastewater treatment is the removal of pollutants to meet given legal effluent standards. To further reduce operators costs additional recovery of resources and energy is desired by industrial and municipal wastewater treatment. Hence the objective in early stage of planning of treatment facilities lies in the identification and evaluation of promising configurations of treatment units. Obviously this early stage of planning may best be supported by software tools to be able to deal with a variety of different treatment configurations. In chemical process engineering various design tools are available that automatically identify feasible process configurations for the purpose to obtain desired products from given educts. In contrast, the adaptation of these design tools for the automatic generation of treatment unit configurations (process chains) to achieve preset effluent standards is hampered by the following three reasons. First, pollutants in wastewater are usually not defined as chemical substances but by compound parameters according to equal properties (e.g. all particulate matter). Consequently the variation of a single compound parameter leads to a change of related parameters (e.g. relation between Chemical Oxygen Demand and Total Suspended Solids). Furthermore, mathematical process models of treatment processes are tailored towards fractions of compound parameters. This hampers the generic representation of these process models which in turn is essential for automatic identification of treatment configurations. Second, treatment technologies for wastewater treatment rely on a variety of chemical, biological, and physical phenomena. Approaches to mathematically describe these phenomena cover a wide range of modeling techniques including stochastic, conceptual or deterministic approaches. Even more the consideration of temporal and spatial resolutions differ. This again hampers a generic representation of process models. Third, the automatic identification of treatment configurations may either be achieved by the use of design rules or by permutation of all possible combinations of units stored within a database of treatment units. The first approach depends on past experience translated into design rules. Hence, no innovative new treatment configurations can be identified. The second approach to identify all possible configurations collapses by extremely high numbers of treatment configurations that cannot be mastered. This is due to the phenomena of combinatorial explosion. It follows therefrom that an appropriate planning algorithm should function without the need of additional design rules and should be able to identify directly feasible configurations while discarding those impractical. This work presents a planning tool for the identification and evaluation of treatment configurations that tackles the before addressed problems. The planning tool comprises two major parts. An external declarative knowledge base and the actual planning tool that includes a goal oriented planning algorithm. The knowledge base describes parameters for wastewater characterization (i.e. material model) and a set of treatment units represented by process models (i.e. process model). The formalization of the knowledge base is achieved by the Web Ontology Language (OWL). The developed data model being the organization structure of the knowledge base describes relations between wastewater parameters and process models to enable for generic representation of process models. Through these parameters for wastewater characterization as well as treatment units can be altered or added to the knowledge base without the requirement to synchronize already included parameter representations or process models. Furthermore the knowledge base describes relations between parameters and properties of water constituents. This allows to track changes of all wastewater parameters which result from modeling of removal efficiency of applied treatment units. So far two generic treatment units have been represented within the knowledge base. These are separation and conversion units. These two raw types have been applied to represent different types of clarifiers and biological treatment units. The developed planning algorithm is based on a Means-Ends Analysis (MEA). This is a goal oriented search algorithm that posts goals from wastewater state and limit value restrictions to select those treatment units only that are likely to solve the treatment problem. Regarding this, all treatment units are qualified according to postconditions that describe the effect of each unit. In addition, units are also characterized by preconditions that state the application range of each unit. The developed planning algorithm furthermore allows for the identification of simple cycles to account for moving bed reactor systems (e.g. functional unit of aeration tank and clarifier). The evaluation of identified treatment configurations is achieved by total estimated cost of each configuration. The planning tool has been tested on five use cases. Some use cases contained multiple sources and sinks. This showed the possibility to identify water reuse capabilities as well as to identify solutions that go beyond end of pipe solutions. Beyond the originated area of application, the planning tool may be used for advanced interrogations. Thereby the knowledge base and planning algorithm may be further developed to address the objectives to identify configurations for any type of material and energy recovery.

Nach einer Betrachtung der bestehenden Ansätze zur automatischen Graphgenerierung wurde eine Möglichkeit entwickelt, auf der Grundlage von OpenStreetMap-Innenraumkarten mit Hilfe einer bereits existierenden Implementierung des Straight Skeleton in Python automatisch Wege zu berechnen und diese wieder ins OpenStreetMap-Format zu überführen.:1. Einleitung 2. Überblick zur aktuellen Forschung 3. Anwendung des Straight Skeleton auf OpenStreetMap-Daten 3.1. Polyskel 3.2. Einlesen der Daten und Extrahieren der Türen und Räume 3.3. Kombination von Türen und Räumen 3.4. Generierung von Wegen mit Hilfe von polyskel 3.5. Kurze Wege zu längeren zusammenfassen 3.6. Wege vereinfachen 3.7. Wege im OpenStreetMap-Format speichern 4. Fazit und Ausblick Literaturverzeichnis Anhang

Microtubules are a part of the cell cytoskeleton that performs different functions, such as providing the mechanical support for the shape of a cell, acting as tracks along which the motor protein move organelles from one part of the cell to another, or the forming mitotic spindle during the cell division. The microtubules are dynamic structures, namely they can grow and shrink. The phase of microtubule growth alternates with the phase of shrinkage that results in the dynamic microtubule network in the cell. However, to form stable and spatially well-defined structures, such as a mitotic spindle, the cell needs to control this stochastic process. This is done by microtubule-associated proteins (MAPs). One class of MAPs is the proteins of XMAP216/Dis1 family, which are microtubule polymerases. The founding member of this family is X. laevis XMAP215. XMAP215 is a processive polymerase acting on the microtubule plus end. XMAP215 binds either directly or reaches the microtubule plus end by the diffusion along the microtubule lattice. Being at the microtubule plus-end XMAP215 stays there transiently and helps to incorporate up to 25 tubulin dimers into microtubule lattice before it dissociates and, therefore, it processively tracks the growing microtubule end during polymerization. There are two hypothesis of microtubule assembly promotion: (i) XMAP215 repeatedly releases an associated tubulin dimer into the microtubule growing plus end or (ii) structurally stabilizes a polymerized tubulin intermediate at the growing plus end and, therefore, preventing depolymerization events. The first way results into the increase of on-rate of tubulin dimers at the microtubule end, whereas the second way results into the decrease of off-rate of tubulin dimers at the microtubule end. Here, I show the study of the mechanism of microtubule growth acceleration by XMAP215 and the dependence of XMAP215 polymerization activity on the applied force. To answer these questions, I investigated the addition of tubulin dimers to the plus end of the microtubule by XMAP215 and how this addition depends on the applied force. XMAP215 remains at the microtubule end for several rounds of tubulin addition surfing both growing and shrinking microtubule ends. Therefore, if one could track the position of the XMAP215 molecules at the very tip of a microtubule with sufficient resolution, it would provide the information about the dynamics of the microtubule end. The technique, which can detect the position of the object of interest with high spatial and temporal resolution in addition to being able to exert a force, is an optical trap. A calibrated optical trap not only provides a good measure of displacement but also enables force measurements. To monitor the position of the molecules of interest, the molecules of interest are usually attached to a microsphere. Hence, I tethered XMAP215 to a microsphere held by an optical trap, and used XMAP215 as a handle to interact with the microtubule tip. When the microtubule grows, the XMAP215 coated microsphere will move in the optical trap and this movement can be detected with high temporal and spatial resolution. My work demonstrates that cooperatively working XMAP215 molecules can not only polymerize microtubule but also harness the energy of microtubule polymerization or depolymerization to transport some cargo. There is an evidence that orthologues of XMAP215 in budding yeasts, fission yeasts and Drosophila localize on the kinetochores. Therefore, the ability of the bearing some load during microtubule polymerization could be potentially important for the XMAP215 functioning during cell division. I also showed the influence of external force applied to the XMAP215 molecules. Pointing toward microtubule growth, a force of 0.5 pN applied to the microtubule tip-coupled XMAP215-coated microsphere increases XMAP215 polymerization activity. However, the force of the same magnitude but applied against microtubule growth does not affect XMAP215 polymerization activity. This result can be explained by the fact, that the force acting in the direction of microtubule growth constrains XMAP215 to be at the very microtubule tip. Hence, XMAP215 can not diffuse away from plus-end and there is higher chance to incorporate tubulin dimers into the microtubule plus-end. The on- and off-rate of tubulin dimers at the microtubule end are both decreased when the external force applied either in direction of microtubule growth or opposite to it. The external force affects the off-rate slightly stronger than on-rate of tubulin dimer. Taking together, my study gives new insights into the mechanism of microtubule polymerization by XMAP215 and shows some novel properties of this protein.

Design idea generation is a significant part of the designer’s work and most frequently associated with creative problem-solving. However, an outstanding challenge in design is translating empirical findings or other inspirational sources into ideas or knowledge that inform design, also known as generating implications for design. Though great efforts have been made to bridge this gap, there is still no overall consensus on how to appropriately incorporate research data and external sources into the design ideas generation process. Besides, design ideas generation is a process that is rooted in personal knowledge. It is often considered a precedent-based type of reasoning where knowledge is continuously transformed to frame new insights and this creative leap across the divide is perceived to be difficult, especially for novice and student designers. Based on the grounded theory, the author conducted open-ended, semi-structured qualitative interviews with eighteen designers, including design professors, practitioners and students to learn about the knowledge on how the contemporary designers select inspirational sources and transform them into ideas with appropriate methods in different contexts, the design mindset involved during the iterative and generative ideation process, and the criteria of evaluating design ideas. The results indicated seven commonly used categories of sources: ‘personal,’ ‘daily stuff,’ ’media,’ ‘technology,’ ‘knowledge of other disciplines,’ ‘fieldwork’ and ‘design practice.’ The methods could be allocated to three stages: investigation, analysis and synthesis. In each specific phase, designers applied different methods to cope with various sources for various purposes, depending on different situations. A general framework was built for designers to conduct a proper selection of sources and methods to transform them into the knowledge that informs design. The study emphasized the flow of inspirational sources, the relationship between sources and methods, and the transformation procedures which tried to help the designer get more scientific structure and give design students more practical guidance in idea generation. The comparison was continuously made within the matrix structure on the matter of sources and methods selection, the role of design research and ideation process among different groups of designers in academics and practice, which revealed the knowledge and skills missed or ignored in university education and challenges in the design project development. The implications for design education were discussed, such as the development of design students’ competence, especially the skills that should be learned in a new digital society to help design education refine idea generation methods and improve corresponding techniques to discover a dynamic balance between theory and practice. / Das Hervorbringen von Designideen ist ein wichtiger Teil der Arbeit eines Designers und wird meistens mit kreativer Problemlösung assoziiert. Eine besondere Herausforderung ist dabei die mentale Verarbeitung von Informationen und Inspirationsquellen und deren Umsetzung zu Gestaltungsentwürfe. Dieser kreative Prozess wird vor allem von Anfängern und Designstudenten als schwierig wahrgenommen. Trotz vielfältiger Forschungsanstrengungen gibt es noch immer keinen allgemeinen Konsens dazu, auf welche Weise Informationen in den Designprozess einbezogen werden und wie sich diese präzedenzbasierte Art des Denkens vermitteln lässt. Da dieser Prozess stark auf Erfahrung basiert, wird er oft als Präzedenz-basierte Art des Denkens betrachtet, bei welcher Wissen fortwährend in neue Erkenntnisse umgeformt wird. Dieser kreative Akt wird vor allem von Anfängern als schwierig wahrgenommen. Dem Paradigma der 'grounded theory' folgend hat die Autorin ergebnisoffene, teilstrukturierte und qualitative Interviews mit Designern durchgeführt. Interviewt wurden Designprofessoren, Praktiker und Studenten, um zu erfahren, wie zeitgenössische Designer Inspirationsquellen recherchieren und mit welchen Methoden sie diese in unterschiedlichen Kontexten zu Ideen umwandeln. Weiterhin werden die Vorgehensweise der Designer während des Gestaltungsprozesses und die Evaluationskriterien der Designideen untersucht. Anhand der Untersuchungsergebnisse lassen sich sieben Informationskategorien unterscheiden: Persönliches, alltägliches, mediales, technologisches Wissen sowie Wissen aus anderen Fachbereichen, aus Feldforschung und aus der Designpraxis. Diese Methoden finden im Designprozess innerhalb von drei Phasen Anwendung: Untersuchung, Analyse und Synthese. In jeder dieser Phase wenden Designer dabei verschiedene Methoden an, um Informationsquellen aufzubereiten und in den Designprozess einzubeziehen. Ausgehend von einem Prozessmodell untersuchte die durchgeführte Studie insbesondere die Einbeziehung von Inspirationsquellen, die Beziehung zwischen Quellen und Methoden und die mentalen Verarbeitungsprozeduren. Mit den erzielten Ergebnissen unterstützt die Untersuchung die Designforschung und gibt Designstudenten eine fundierte Anleitung für die Entwicklung von Ideen. Ein besonderer Fokus der Studie lag auf dem Vergleich von verschiedenen Gruppen von Designern – insbesondere auch mit Blick auf den Unterschied zwischen tären Ausbildung vermittelten Kompetenzen mit den praktischen Anforderungen korrespondieren. Darauf aufbauend diskutiert diese Arbeit Empfehlungen für die Designausbildung, z.B. hinsichtlich des Kontexts der digitalen Gesellschaft oder der Verfeinerung von Methoden der Ideengenerierung. Damit soll die Arbeit dazu beitragen, die Designausbildung zu verbessern und ein dynamisches Gleichgewicht zwischen Theorie und Praxis zu ermöglichen.

In model-driven engineering, powerful query/view languages exist to compute result sets/views from underlying models. However, to use these languages effectively, one must understand the query/view language concepts as well as the underlying models and metamodels structures. Consequently, it is a challenge for domain experts to create queries/views due to the lack of knowledge about the computer-internal abstract representation of models and metamodels. To better support domain experts in the query/view creation, the goal of this paper is the presentation of a generic concept to specify queries/views on models without requiring deep knowledge on the realization of modeling languages. The proposed concept is agnostic to specific modeling languages and allows the query/view generation by-example with a simple mechanism for filtering model elements. Based on this generic concept, a generic query/view language is proposed that uses role-oriented modeling for its non-intrusive application for specific modeling languages. The proposed language is demonstrated based on the role-based single underlying model (RSUM) approach for AutomationML to create queries/views by-example, and subsequently, associated viewtypes to modify the result set or view.