Universität Stuttgart
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Item Open Access Effiziente Gestaltung und Anwendung von attributbasierter Zugriffskontrolle für RESTful Services(2019) Hüffmeyer, Marc; Mitschang, Bernhard (Prof. Dr.)Item Open Access Ansätze für flexible und fehlertolerante modellgetriebene IoT-Anwendungen in dynamischen Umgebungen(2024) Del Gaudio, Daniel; Mitschang, Bernhard (Prof. Dr.-Ing. habil.)Item Open Access Supporting multi-tenancy in Relational Database Management Systems for OLTP-style software as a service applications(2015) Schiller, Oliver; Mitschang, Bernhard (Prof. Dr.-Ing. habil.)The consolidation of multiple tenants onto a single relational database management system (RDBMS) instance, commonly referred to as multi-tenancy, turned out being beneficial since it supports improving the profit margin of the provider and allows lowering service fees, by what the service attracts more tenants. So far, existing solutions create the required multi-tenancy support on top of a traditional RDBMS implementation, i. e., they implement data isolation between tenants, per-tenant customization and further tenant-centric data management features in application logic. This is complex, error-prone and often reimplements efforts the RDBMS already offers. Moreover, this approach disables some optimization opportunities in the RDBMS and represents a conceptual misstep with Separation of Concerns in mind. For the points mentioned, an RDBMS that provides support for the development and operation of a multi-tenant software as a service (SaaS) offering is compelling. In this thesis, we contribute to a multi-tenant RDBMS for OLTP-style SaaS applications by extending a traditional disk-oriented RDBMS architecture with multi-tenancy support. For this purpose, we primarily extend an RDBMS by introducing tenants as first-class database objects and establishing tenant contexts to isolate tenants logically. Using these extensions, we address tenant-aware schema management, for which we present a schema inheritance concept that is tailored to the needs of multi-tenant SaaS applications. Thereafter, we evaluate different storage concepts to store a tenant’s tuples with respect to their scalability. Next, we contribute an architecture of a multi-tenant RDBMS cluster for OLTP-style SaaS applications. At that, we focus on a partitioning solution which is aligned to tenants and allows obtaining independently manageable pieces. To balance load in the proposed cluster architecture, we present a live database migration approach, whose design favors low migration overhead and provides minimal interruption of service.Item Open Access Scalable traffic engineering heuristics for time-triggered communication in real-time networks(2026) Geppert, Heiko; Rothermel, Kurt (Prof. Dr. rer. nat. Dr. h. c.)Distributed safety-critical cyber-physical systems require real-time behavior. This means they must respond not just quickly, but in time, to new situations considering both, the task processing and network communication time. From a networking perspective, meticulous, time-driven traffic planning performed at the frame level is necessary to guarantee low end-to-end delay bounds and low latency. This involves carefully planning transmission operations along each time-critical frame's network path are carefully planned, including precise timing, to limit or even eliminate interference from cross-traffic and ensure timely delivery. Since modern real-time systems can consist of hundreds or thousands of devices - for example, large manufacturing plants or continental-sized power grids - the traffic planning must be highly scalable. Although there are many traffic planning approaches in the literature, there is a lack of very fast heuristics that can handle very large stream sets and networks quickly. This thesis investigates traffic planning heuristics and optimization techniques, focusing on different aspects of the traffic planning domain. The traffic planning consists of novel methods for conflict-graph-based scheduling and new heuristics for very large instances of traffic planning problem. The optimizations include multicast partitioning, which combines the benefits of multicast and unicast traffic plans, and load-balanced stream placement, which generates traffic plans that can accommodate additional streams joining the system later. We created prototype implementations and analyzed their performance in solving the traffic planning problem. Our traffic plans yielded a higher accumulated network throughput or admitted more streams while maintaining computation times ranging from sub-seconds to minutes, even for extremely large-scale problem instances. The traffic planning methods and optimization techniques presented in this thesis can be applied to modern real-time networking technologies, such as Time-Sensitive Networking and TTEthernet.Item Open Access Scalable biophysical simulations of the neuromuscular system(2021) Maier, Benjamin; Schulte, Miriam (Prof. Dr.)The human neuromuscular system consisting of skeletal muscles and neural circuits is a complex system that is not yet fully understood. Surface electromyography (EMG) can be used to study muscle behavior from the outside. Computer simulations with detailed biophysical models provide a non-invasive tool to interpret EMG signals and gain new insights into the system. The numerical solution of such multi-scale models imposes high computational work loads, which restricts their application to short simulation time spans or coarse resolutions. We tackled this challenge by providing scalable software employing instruction-level and task-level parallelism, suitable numerical methods and efficient data handling. We implemented a comprehensive, state-of-the-art, multi-scale multi-physics model framework that can simulate surface EMG signals and muscle contraction as a result of neuromuscular stimulation. This work describes the model framework and its numerical discretization, develops new algorithms for mesh generation and parallelization, covers the use and implementation of our software OpenDiHu, and evaluates its computational performance in numerous use cases. We obtain a speedup of several hundred compared to a baseline solver from the literature and demonstrate, that our distributed-memory parallelization and the use of High Performance Computing resources enables us to simulate muscular surface EMG of the biceps brachii muscle with realistic muscle fiber counts of several hundred thousands. We find that certain model effects are only visible with such high resolution. In conclusion, our software contributes to more realistic simulations of the neuromuscular system and provides a tool for applied researchers to complement in vivo experiments with in-silico studies. It can serve as a building block to set up comprehensive models for more organs in the musculoskeletal system.Item Open Access Information driven exploration in robotics(2016) Kulick, Johannes; Toussaint, Marc (Prof. Dr.)Imagine an intelligent robot entering an unknown room. It starts interacting with its new surroundings to understand what properties the new objects have and how they interact with each other. Finally, he gathered enough information to skillfully perform various tasks in the new environment. This is the vision behind our research towards intelligent robots. An important role in the described behavior is the ability to chose actions in order to learn new things. This ability we call exploration. It enables the robot to quickly learn about the properties of the objects. Surprisingly autonomous exploration has been mostly neglected by robotics research so far, because many fundamental problems like motor control and perception were still not satisfactory solved. The developments of recent years have, however, overcome this hurdle. State of the art methods enable us now to conduct research on exploration in robotics. On the other hand the machine learning and statistics community has developed methods and the theoretical background to lead learning algorithms to the most promising data. Under the terms active learning and experimental design many methods have been developed to improve the learning rate with fewer training data. In this thesis we combine results from both fields to develop a framework of exploration in robotics. We base our framework on the notion of information and information gain, developed in the field of information theory. And although we show that optimal exploration is a computational hard problem, we develop efficient exploration strategies using information gain as measure and Bayesian experimental design as foundation. To test the explorative behavior generated by our strategies we introduce the Physical Exploration Challenge. It formalizes the desired behavior as exploration of external degrees of freedom. External degrees of freedom are those the robot can not articulate directly but only by interacting with the environment. We present how we can model different exploration tasks of external degree of freedom: Exploring the meaning of geometric symbols by moving objects, exploring the existence of joints and their properties, and exploring how different joints in the environment are interdependent. Different robots show these exploration tasks in both simulated and real world experiments.Item Open Access Efficient code offloading techniques for mobile applications(2017) Berg, Florian; Rothermel, Kurt (Prof. Dr. rer. nat. Dr. h. c.)Since the release of the first smart phone from Apple in the year 2007, smart phones in general experience a fast growth of rising popularity. A smart phone typically possesses among others a touchscreen display as user interface, a mobile communication for accessing the Internet, and a System-on-a-Chip as an integrated circuit of required components like a central processing unit. This pervasive computing platform derives its required power from a battery, where an end user runs upon it different kinds of applications like a calendar application or a high-end mobile game. Differing in the usage of the local resources from a battery-operated smart phone, a heavy utilization of local resources like playing a resource-demanding application drains the limited resource of energy in few hours. Despite the constant increase of memory, communication, or processing capabilities of a smart phone since the release in 2007, applications are also getting more and more sophisticated and demanding. As a result, the energy consumed on a smart phone was, still is, and will be its main limiting factor. To prevent the limited resource of energy from a quick exhaustion, researchers propose code offloading for (resource-constrained) mobile devices like smart phones. Code offloading strives for increasing the energy efficiency and execution speed of applications by utilizing a server instance in the infrastructure. To this end, a code offloading approach executes dynamically resource-intensive parts from an application on powerful remote servers in the infrastructure on behalf of a (resource-constrained) mobile device. During the remote execution of a resource-intensive application part on a remote server, a mobile device only waits in idle mode until it receives the result of the application part executed remotely. Instead of executing an application part on its local resources, a (resource-constrained) mobile device benefits from the more powerful resources of a remote server by sending the information required for a remote execution, waiting in idle mode, and receiving the result of the remote execution. The process of offloading code from a (resource-constrained) mobile device to a powerful remote server in the infrastructure, however, faces different problems. For instance, code offloading introduces some overhead for additional computation and communication on a mobile device. Moreover, spontaneous disconnections during a remote execution can cause a higher energy consumption and execution time than a local execution on a mobile device without code offloading. To this end, this dissertation addresses the whole process of offloading code from a mobile device not only to one but also to multiple remote resources, comprising the following steps: 1) First, code offloading has to identify feasible parts from an application for a remote execution, where the distributed execution of the identified application part is more beneficial than its local execution. A feasible part for a remote execution typically has the following properties: A low size of information required for transmission before a remote execution, a resource-intensive computation not accessing local sensors, and a low size of information required for transmission after a remote execution. In the area of identification of application parts for a remote execution, this dissertation presents an approach based on code annotations from application developers that automatically transforms a monolithic execution on a mobile device to a distributed execution on multiple heterogeneous resources. In contrast to related approaches in the literature, the annotation-based approach requires least interventions from application developers and end users, keeping the overhead introduced on a mobile device low. 2) For an application part identified for a remote execution, code offloading has to determine its execution side, executing the application part either on the local resources of a mobile device or on the remote resource at the infrastructure. In the area of determining the execution side for an application part, this dissertation presents the offloading problem, where a mobile device decides whether to execute an application part locally or remotely. Furthermore, this dissertation also presents an approach called "code bubbling" that shifts the decision making into the infrastructure. In contrast to related approaches in the literature, the decision-based approach on a mobile device and the bubbling-based approach minimize the execution time, energy consumption, and monetary cost for an application. 3) To determine the execution side for an application part identified for a remote execution, code offloading has to obtain different parameters from the application, participating resources, and utilized links. In the area of obtaining the information required from an application, this dissertation presents a bit-flipping approach that dynamically flips a bit at the modification of application-related information. Furthermore, this dissertation also presents an offload-aware Application Programming Interface (API) that encapsulates the application-related information required for code offloading. In contrast to related approaches in the literature, the bit-flipping approach and the offload-aware API provide an efficient gathering of information at run-time, keeping the overhead introduced on a mobile device low. 4) Beside the information from an application, code offloading has to obtain further information from participating resources and utilized links. In the area of obtaining the information required from participating resources and utilized links, this dissertation presents the approach of code bubbling, already mentioned above. In contrast to related approaches in the literature, the bubbling-based approach makes the offload decision at the place where the related information occurs, keeping the overhead introduced on a mobile device, participating resources, and utilized links low. 5) In case of a remote execution of an application part, code offloading has to send the information required for a remote execution to the remote resource that subsequently executes the application part on behalf of the mobile device. In the area of sending the required information and executing an application part remotely, this dissertation presents code offloading with a cache on the remote side. The cache on the remote side serves as a collective storage of results for already executed application parts, avoiding a repeated execution of previously run application parts. In contrast to related approaches in the literature, the caching-aware approach increases the efficiency of code offloading, keeping the energy consumption, execution time, and monetary cost low. 6) While a remote resource executes an application part, code offloading has to handle the occurrence of failures like a failure of the remote resource or a disconnection. In the area of handling the occurrence of failures, this dissertation presents a preemptable offloading of code with safe-points. The preemptable offloading of code with safe-points enables an interruption of an offloading process and a corresponding continuation of a remote execution on a mobile device, without abandoning the complete result calculated remotely so far. Based on a preemptable offloading of code with safe-points, this dissertation further presents a predictive offloading of code with safe-points that minimizes the overhead introduced by safe-point'ing and maximizes the efficiency of a deadline-aware offloading. In contrast to related approaches in the literature, the preemptable approach with safe-point'ing increases the robustness of code offloading in case of failures. Furthermore, the predictive approach for safe-point'ing ensures a minimal responsiveness and a maximal efficiency of applications despite failures. 7) At the end of a remote execution of an application part, code offloading has to gather on the remote resource the required information after the execution and send this information to the mobile device. In the area of gathering the required information, a remote resource utilizes the same approaches as a mobile device, already mentioned above (cf. the bit-flipping approach and the offload-aware API). 8) Last, code offloading has to receive on the mobile device the information from a remote resource, install the information on the mobile device, and continue the execution of the application on the mobile device. In the area of installing the information and continuing the execution locally, a mobile device utilizes the approaches already mentioned above (cf. the bit-flipping approach and the offload-aware API).Item Open Access Neural network full-body human predictive models and their use for coordinated robot motion planning(2022) Kratzer, Philipp; Toussaint, Marc (Prof. Dr.)Item Open Access The Enterprise Data Marketplace : a platform for democratizing company data(2024) Eichler, Rebecca; Mitschang, Bernhard (Prof. Dr.-Ing. habil.)In the era of big data, multitudes of data are generated and collected in companies. This data contains a potential value that can be leveraged to gain new insights, e.g., for enhancing business models or reengineering industrial products. Extracting data value requires that this data is available for use. Yet, studies show that significant amounts of the data remain unused in companies. In this regard, data democratization initiatives, with the goal of empowering company employees to find, understand, access, use, and share data, are gaining in importance. Towards this end, data marketplaces are studied as metadata-based platforms to facilitate the exchange and provisioning of data and data-related services. However, data marketplaces are mainly investigated for the exchange of data and services between organizations or private individuals, i.e., as external data marketplaces. Little research focuses on the use of data marketplaces in the company-internal context, i.e., as an Enterprise Data Marketplace (EDMP). Topics of how the EDMP differs from an external marketplace, the scope of its offerings and functionality, challenges that arise in the company-internal context, or how the EDMP can be embedded in and leverage the existent company system and tool landscape have not been investigated in detail thus far. In this thesis, the Enterprise Data Marketplace is examined as a platform for democratizing data in companies, and in this context, the above-listed gaps are addressed. To this end, four research goals (RGs) are put forward: (RG1) the identification of the processes and challenges company employees face in finding, understanding, accessing, and sharing data in the enterprise without an EDMP; (RG2) the identification of the distinctive aspects of an EDMP; (RG3) establishing an architectural foundation for building an EDMP; and (RG4) the goal of leveraging existent metadata in the company tool and system landscape in the EDMP. The research goals are covered through nine research contributions. These entail the current data provider and data consumer journeys and challenges, an EDMP type distinction based on an EDMP definition, as well as a presentation of its distinctive characteristics, requirements, and challenges. An enterprise integration and platform architecture, together with an approach for leveraging existent metadata, yields the foundation for building an EDMP. The feasibility of the concepts put forward in this thesis is demonstrated through an EDMP prototype and an evaluation based on an experiment and qualitative assessment. The evaluation yields that the EDMP is well-suited for the effective realization of data democratization within companies and that it not only addresses several of the current issues data providers and consumers face but also increases the efficiency and reduces the complexity in accessing data. This thesis therefore introduces the EDMP as a platform for democratizing company data and lays the foundation for establishing the EDMP within a company.Item Open Access Data-efficient and safe learning with Gaussian processes(2020) Schreiter, Jens; Toussaint, Marc (Prof. Dr. rer. nat.)Data-based modeling techniques enjoy increasing popularity in many areas of science and technology where traditional approaches are limited regarding accuracy and efficiency. When employing machine learning methods to generate models of dynamic system, it is necessary to consider two important issues. Firstly, the data-sampling process should induce an informative and representative set of points to enable high generalization accuracy of the learned models. Secondly, the algorithmic part for efficient model building is essential for applicability, usability, and the quality of the learned predictive model. This thesis deals with both of these aspects for supervised learning problems, where the interaction between them is exploited to realize an exact and powerful modeling. After introducing the non-parametric Bayesian modeling approach with Gaussian processes and basics for transient modeling tasks in the next chapter, we dedicate ourselves to extensions of this probabilistic technique to relevant practical requirements in the subsequent chapter. This chapter provides an overview on existing sparse Gaussian process approximations and propose some novel work to increase efficiency and model selection on particularly large training data sets. For example, our sparse modeling approach enables real-time capable prediction performance and efficient learning with low memory requirements. A comprehensive comparison on various real-world problems confirms the proposed contributions and shows a variety of modeling tasks, where approximate Gaussian processes can be successfully applied. Further experiments provide more insight about the whole learning process, and thus a profound understanding of the presented work. In the fourth chapter, we focus on active learning schemes for safe and information-optimal generation of meaningful data sets. In addition to the exploration behavior of the active learner, the safety issue is considered in our work, since interacting with real systems should not result in damages or even completely destroy it. Here we propose a new model-based active learning framework to solve both tasks simultaneously. As basis for the data-sampling process we employ the presented Gaussian process techniques. Furthermore, we distinguish between static and transient experimental design strategies. Both problems are separately considered in this chapter. Nevertheless, the requirements for each active learning problem are the same. This subdivision into a static and transient setting allows a more problem-specific perspective on the two cases, and thus enables the creation of specially adapted active learning algorithms. Our novel approaches are then investigated for different applications, where a favorable trade-off between safety and exploration is always realized. Theoretical results maintain these evaluations and provide respectable knowledge about the derived model-based active learning schemes. For example, an upper bound for the probability of failure of the presented active learning methods is derived under reasonable assumptions. Finally, the thesis concludes with a summary of the investigated machine learning problems and motivate some future research directions.