Systematic measuring of hydraulic machine's performance data at hydraulic model test rigs involves comprehensive expert knowledge of the test rig engineer and is time-consuming, as the multidimensional operation space must be explored manually. Expertise is needed to account for specific performance limits of test-rig equipment, model turbines, storage pumps, or pump turbines as well as some hysteresis of the setup due to flow separation and dealing with operation point-dependent gains between controlled parameters and measured quantities. To free valuable manpower from the burden of shift work, the measurement procedure is subject to automation. However, testing new automation concepts involves safety-critical or expensive tests and consumes valuable time at tests too. To develop and validate new automation concepts offline, we present a hybrid data-driven modeling approach to create a digital twin of the combined setup of a hydraulic turbine on a hydraulic test rig. Special attention paid to the modeling approach is consistent with existing mathematical relations governed by either physical models or norms. The presented approach allows the separation of test rig and hydraulic model machine under test and this enables offline validation of automation concepts on different combinations of test rigs and hydraulic model machines. The modeling approach is demonstrated by performing cross-validation using recorded measurement data.

Abstract As part of the model acceptance testing according to IEC 60193:2019, it is required to determine the main hydraulic performance guarantee points. This usually involves extensive expert knowledge and is time consuming. To lessen the burden of testing engineers and reduce shifts, new automation strategies are needed. One of these strategies involves creating a system that replicates the tedious task of operators analyzing the machine’s characteristics manually and allows them to supervise the test procedure more efficiently. However, this provides us with new challenges as such systems have to rely on the provided data instead of longstanding experience, and although there is prior knowledge available from former tests regarding the behavior of the hydraulic, the prediction from prior models is based on hydraulic similarity and can only give a rough estimation. To detect areas of the characteristic points automatically, we present an approach consisting of predictive models and mathematical criteria to minimize the search region. We propose to use data from pre-measurements that are recorded quickly but do not adhere to the IEC norm. Nevertheless, the acquired data can be leveraged to build mathematical models with statistical methods on which search criteria can be applied. Several examples with real life data are demonstrated to validate the method. Additionally, automation requisites like starting and stopping are discussed.

Computer simulation has become a vital tool for modeling complex systems. However, the development and deployment of simulation models often involve multiple stages, tools, and teams, which can lead to significant challenges in maintaining quality, reliability, and efficiency. DevOps, a set of practices that combines software development and IT operations, has emerged as a promising approach to streamline the simulation development. However, most system engineers are not DevOps specialists and there are a lot of manual steps involved when writing build pipelines and configurations of simulations. For this purpose, an abstract graph-based meta-data model was presented in previous work to provide an automation framework for DevOps with simulations. In this work we want to continue our investigations by expanding and harmonizing this approach to better work with established standards like SSP, SysML2 and DCP and demonstrating it's application on real-life use cases.

In this chapter, we present an overview of different group recommender applications. We organize this overview into the application domains of music, movies and TV programs, travel destinations and events, news and web pages, healthy living, software engineering, and domain-independent recommenders. Each application is analyzed with regard to the characteristics of group recommenders as introduced in Chap. 2 .

With the goal of improving the quality of model-based development and to reduce testing effort, DevOps practices have gained more and more importance. However, most system engineers are not DevOps specialists, and there are a lot of manual steps involved when writing build pipelines and configurations of simulations. For this purpose, an abstract graph-based metadata model is proposed. This allows the autogeneration of scenario descriptions for simulations and code for the build server where the simulation environment is set up and executed. This is demonstrated by applying this process to the DCP standard. In this paper, we will discuss three simple use cases which are motivated by practical problems that arise in complex development environments and how the proposed solutions can be used to tackle them. Detailed descriptions and implementations of the use cases show how the proposed methods can be applied in practice and help solve the described problems. Furthermore, a Python implementation of a DCP master and a simple FMI-to-DCP wrapper are presented in this work.

In applied mathematics, especially in optimization, functions are often only provided as so called "Black-Boxes" provided by software packages, or very complex algorithms, which make automatic differentation very complicated or even impossible. Hence one seeks the numerical approximation of the derivative. Unfortunately numerical differentation is a difficult task in itself, and it is well known that it is numerical instable. There are many works on this topic, including the usage of (global) Chebyshev approximations. Chebyshev approximations have the great property that they converge very fast, if the function is smooth. Nevertheless those approches have several drawbacks, since in practice functions are not smooth, and a global approximation needs many function evalutions. Nevertheless there is hope. Since functions in real world applications are most times smooth except for finite points, corners or edges. This motivates to use a local Chebyshev approach, where the function is only approximated locally, and hence the Chebyshev approximations still yields a fast approximation of the desired function. We will study such an approch in this work, and will provide a numerical example

In complex mechatronic systems, co-simulations need to be conducted to test the behaviour of larger systems. DevOps tools and methods are used to make the development of simulation models for mechatronic systems efficient. For this purpose, a graph-based metadata model is introduced to describe the process to autogenerate code for build servers. It also makes build pipelines reusable and suitable for safety critical software components. We discuss how DevOps processes for mechatronic systems are modelled as a graph, define the data structure formally and present algorithms for automatic code generation and representation within a NoSQL graph database. In addition, we compare different optimisation strategies for build and deployment schedules.

Constraint-based recommenders support users in the identification of items (products) fitting their wishes and needs. Example domains are financial services and electronic equipment. In this paper we show how divide-and-conquer based (direct) diagnosis algorithms (no conflict detection is needed) can be exploited in constraint-based recommendation scenarios. In this context, we provide an overview of the MediaWiki-based recommendation environment WeeVis.

In the design procedures of complex mechanical systems, a plurality of different design simulations and functional test simulations have to be conducted. In this paper we discuss how to make the continuous integration of simulation components for large co-simulations feasible and present a graph-based meta data model for the continuous integration of simulations. This new concept reduces the continuous integration efforts for developers by providing tools and a framework for the automated generation of continuous integration pipelines.

Many domains require scalable algorithms that help to determine diagnoses efficiently and often within predefined time limits. Anytime diagnosis is able to determine solutions in such a way and thus is especially useful in real-time scenarios such as production scheduling, robot control, and communication networks management where diagnosis and corresponding reconfiguration capabilities play a major role. Anytime diagnosis in many cases comes along with a trade-off between diagnosis quality and the efficiency of diagnostic reasoning. In this paper we introduce and analyze FLEXDIAG which is an anytime direct diagnosis approach. We evaluate the algorithm with regard to performance and diagnosis quality using a configuration benchmark from the domain of feature models and an industrial configuration knowledge base from the automotive domain. Results show that FLEXDIAG helps to significantly increase the performance of direct diagnosis search with corresponding quality tradeoffs in terms of minimality and accuracy. Alexander Felfernig Applied Software Engineering Group, Institute for Software Technology, TU Graz, Austria E-mail: alexander.felfernig@ist.tugraz.at Rouven Walter Symbolic Computation Group, WSI Informatics, Universität Tübingen, Germany E-mail: rouven.walter@uni-tuebingen.de José A. Galindo Computer Languages and Systems Department, University of Sevilla, Spain E-mail: jagalindo@us.es David Benavides Computer Languages and Systems Department, University of Sevilla, Spain E-mail: benavides@us.es Seda Polat-Erdeniz Applied Software Engineering Group, Institute for Software Technology, TU Graz, Austria E-mail: spolater@ist.tugraz.at Müslüm Atas Applied Software Engineering Group, Institute for Software Technology, TU Graz, Austria E-mail: muatas@ist.tugraz.at Stefan Reiterer SelectionArts, Austria E-mail: stefan.reiterer@selectionarts.com 2 A. Felfernig et al.

Many domains require scalable algorithms that help to determine diagnoses efficiently and often within predefined time limits. Anytime diagnosis is able to determine solutions in such a way and thus is especially useful in real-time scenarios such as production scheduling, robot control, and communication networks management where diagnosis and corresponding reconfiguration capabilities play a major role. Anytime diagnosis in many cases comes along with a trade-off between diagnosis quality and the efficiency of diagnostic reasoning. In this paper we introduce and analyze FlexDiag which is an anytime direct diagnosis approach. We evaluate the algorithm with regard to performance and diagnosis quality using a configuration benchmark from the domain of feature models and an industrial configuration knowledge base from the automotive domain. Results show that FlexDiag helps to significantly increase the performance of direct diagnosis search with corresponding quality tradeoffs in terms of minimality and accuracy.

The Internet Of Things (IoT) is an emerging paradigm that envisions a networked infrastructure enabling different types of devices to be interconnected. It creates different kinds of artifacts (e.g., services and applications) in various application domains such as health monitoring, sports monitoring, animal monitoring, enhanced retail services, and smart homes. Recommendation technologies can help to more easily identify relevant artifacts and thus will become one of the key technologies in future IoT solutions. In this article, we provide an overview of existing applications of recommendation technologies in the IoT context and present new recommendation techniques on the basis of real-world IoT scenarios.

Group Recommender Systems aim to support the identification of items that best fit individual preferences of group members. However, decision making behavior of group members can be affected by decision biases which can deteriorate group decision quality. In this paper, we analyze the existence of Group Polarization Effects in two different domains and present a way to counteract these effects. Group Polarization is the tendency of a group to make decisions that are more extreme than the average of individual group members' preferences. We analyze Group Polarization in the context of risk analysis and cost estimation. In risk related group decisions, we figured out that if individual group members tend to make cautious decisions, then the group decision will be more cautious. However, in decisions related to cost estimation, the group estimations are lower than the average of group members' estimations (i.e., cautious shift). Furthermore, our results show that individual group members with diverse preferences are not influenced by Group Polarization Effects. The diversity in preferences of individual group members helps to counteract Group Polarization Effects.

In this chapter, we present an overview of different group recommender applications. We organize this overview into the application domains of music, movies and TV programs, travel destinations and events, news and web pages, healthy living, software engineering, and domain-independent recommenders. Each application is analyzed with regard to the characteristics of group recommenders as introduced in Chapter 2.

PeopleViews is a Human Computation based environment for the construction of constraint-based recommenders. Constraint-based recommender systems support the handling of complex items where constraints (e.g., between user requirements and item properties) can be taken into account. When applying such systems, users are articulating their requirements and the recommender identifies solutions on the basis of the constraints in a recommendation knowledge base. In this paper, we provide an overview of the PeopleViews environment and show how recommendation knowledge can be collected from users of the environment on the basis of micro-tasks. We also show how PeopleViews exploits this knowledge for automatically generating recommendation knowledge bases. In this context, we compare the prediction quality of the recommendation approaches integrated in PeopleViews using a DSLR camera dataset.

E-learning environments provide an orthogonal approach to transfer relevant knowledge. For example, sales representatives can improve their sales knowledge more independently from related courses offered. Major challenges for successfully establishing e-learning technologies in a company are to develop learning content in an efficient fashion, to recommend only relevant content to system users, and to motivate them to utilize the learning environment in a sustainable fashion. In this paper, we present the gamification-based e-learning environment STUDYBATTLES. We provide an overview of STUDYBATTLES functionalities including content creation, gamification techniques, learning performance analysis, and automated question generation. We show how STUDYBATTLES can be utilized for different learning purposes in academic and professional environments. In addition, we introduce an approach to automatically generate product and sales domain learning content from recommender knowledge bases to be exploited in STUDYBATTLES. Finally, we report the results of an initial qualitative study related to the applicability of STUDYBATTLES in different domains, the potential improvements that StudyBattles can achieve, and additional functionalities that should be integrated.

E-learning environments provide an orthogonal approach to transfer relevant knowledge. For example, sales representatives can improve their sales knowledge more independently from related courses offered. Major challenges for successfully establishing elearning technologies in a company are to develop learning content in an efficient fashion, to recommend only relevant content to system users, and to motivate them to utilize the learning environment in a sustainable fashion. In this paper, we present the gamification-based e-learning environment STUDYBATTLES. We provide an overview of STUDYBATTLES functionalities including content creation, gamification techniques, learning performance analysis, and automated question generation. We show how STUDYBATTLES is and can be utilized for different learning purposes in academic and professional environments. In addition, we introduce an approach to automatically generate product and sales domain learning content from recommender knowledge bases to be exploited in STUDYBATTLES. Finally, we report the results of an initial qualitative study related to the applicability of STUDYBATTLES in different domains, the potential improvements that STUDYBATTLES can achieve, and additional functionalities that should be integrated. Keywords– gamification-based e-learning; automated question generation; knowledge-based recommender systems; constraint satisfaction problem; knowledge acquisition