05 Fakultät Informatik, Elektrotechnik und Informationstechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6
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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 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 Stable and mass-conserving high-dimensional simulations with the sparse grid combination technique for full HPC systems and beyond(2024) Pollinger, Theresa; Pflüger, Dirk (Prof. Dr.)In the light of the ongoing climate crisis, mastering controlled plasma fusion has the potential to be one of the pivotal scientific achievements of the 21st century. To understand the turbulent fields in confined fusion devices, simulation has been and continues to be both an asset and a challenge. The main limiting factor to large-scale high-fidelity predictive simulations lies in the Curse of Dimensionality, which dominates all grid-based discretizations of plasmas based on the Vlasov-Poisson and Vlasov-Maxwell equations. In the full formulation, they result in six-dimensional grids and fine scales that need to be resolved, leading to a potentially untractable number of degrees of freedom. Typical approaches to this problem - coordinate transformations such as gyrokinetics, grid adaptation, restricting oneself to limited resolutions - do not directly address the Curse of Dimensionality, but rather work around it. The sparse grid combination technique, which forms the center of this work, is a multiscale approach that alleviates the curse of dimensionality for time-stepping simulations: Multiple regular grid-based simulations are run and update each other’s information throughout the course of simulation time. The present thesis improves upon the former state-of-the-art of the combination technique in three ways: introducing conservation of mass and numerical stability through the use of better-suited multiscale basis functions, optimizing the code for large-scale HPC systems, and extending the combination technique to the widely-distributed setting. Firstly, this thesis analyzes the often-used hierarchical hat function from the viewpoint of biorthogonal wavelets, which allows to replace the hierarchical hat function by other multiscale functions (such as the mass-conserving CDF wavelets) in a straightforward manner. Numerical studies presented in the thesis show that this not only introduces conservation but also increases accuracy and avoids numerical instabilities - which previously were a major roadblock for large-scale Vlasov simulations with the combination technique. Secondly, the open-source framework DisCoTec was extended to scale the combination technique up to the available memory of entire supercomputing systems. DisCoTec is designed to wrap the combination technique around existing grid-based solvers and draws on the inherent parallelism of the combination technique. Among several other contributions, different communication-avoiding multiscale reduction schemes were developed and implemented into DisCoTec as part of this work. The scalability of the approach is asserted by an extensive set of measurements in this thesis: DisCoTec is shown to scale up to the full system size of four German supercomputers, including the three CPU-based Tier-0/Tier-1 systems. Thirdly, the combination technique was further extended to the widely-distributed setting, where two HPC systems synchronously run a joint simulation. This is enabled by file transfer as well as sophisticated algorithms for assigning the different simulation instances to the systems, two of which were developed as part of this work. By the resulting drastic reductions in the communication volume, tolerable transfer times for combination technique simulations on different HPC systems have been achieved for the first time. These three advances - improved numerical properties, scaling efficiently up to full system sizes, and the possibility to extend the simulation beyond a single system - show the sparse grid combination technique to be a promising approach for future high-fidelity simulations of higher-dimensional problems, such as plasma turbulence.Item Open Access Adaptive human-robot policy blending for shared control teleoperation(2024) Oh, Yoojin; Toussaint, Marc (Prof. Dr. rer. nat.)Item Open Access Democratizing clustering analyses : AutoML, meta-learning, and ensemble clustering to support novice analysts(2024) Treder-Tschechlov, Dennis; Schwarz, Holger (apl. Prof. Dr.)Data Analysis is a crucial discipline to gain insights from data. A fundamental primitive is clustering analysis, which groups instances such that each group has similar instances (homogeneity) and such that the groups are as dissimilar as possible (heterogeneity). Clustering is used in many different application domains and throughout these manifold application domains, analysts struggle to select an appropriate clustering configuration, i.e., a setting of clustering algorithm and its hyperparameters. To find a clustering configuration, analysts have to perform multiple exploration cycles, i.e., select, execute, and evaluate various configurations. This process is time-consuming and its success depends on the domain expertise of an analyst. Novice analysts, in particular, lack the required domain knowledge and experience. They do not know how to explore configurations efficiently (Challenge C1), cannot benefit from experience, e.g., to select a cluster validity index to evaluate the results (Challenge C2), and face high runtimes when executing complex clustering algorithms with quadratic or higher runtimes, e.g., density-based clustering (Challenge C3). Thus, they have to execute many different clustering configurations with potentially high runtimes and can still end up with poor results. In contrast, experienced analysts have profound knowledge about the data characteristics in their domain and therefore have to execute only a few configurations to achieve accurate results. This thesis proposes the following approaches to support novice analysts to achieve similar results to those of more experienced analysts: (1) AutoML4Clust: Recent works in the area of AutoML are able to explore large configuration spaces efficiently using various optimization techniques. However, these approaches are only applied to supervised learning tasks, and applying such concepts to clustering is challenging due to its unsupervised nature. Thus, it is not clear how to use such optimizers for clustering and which cluster validity index is suitable as optimization objective. Therefore, to address Challenge C1, we propose AutoML4Clust, an efficient AutoML for clustering system. We apply optimizers from existing AutoML systems to the unsupervised task of clustering. AutoML4Clust is generic as it supports four different optimizers and three different cluster validity indices. Our evaluation reveals promising combinations of optimizers and cluster validity indices. Furthermore, the choice of the cluster validity index is more crucial than the choice of the optimizer and it depends on the data characteristics. (2) ML2DAC: While AutoML4Clust addresses the efficient exploration, the analyst has to specify the cluster validity index and the search space of clustering algorithms. In contrast to more experienced analysts, novice analysts have no experience in specifying the cluster validity index or narrowing down the search space. We therefore propose the meta-learning approach ML2DAC to address Challenge C2, i.e., enabling novice analysts to benefit from external experience. ML2DAC extends AutoML4Clust with a novel meta-learning methodology to learn from previously evaluated clustering analyses and automatically apply them to new datasets. To this end, ML2DAC requires a previous learning phase to build a meta-knowledge repository. This learning phase is executed once and can then be applied to any new dataset. When applying our approach to a new dataset, we use the meta-knowledge repository to (i) narrow down the search space to a few algorithms, (ii) recommend well-performing configurations, and (iii) select a suitable cluster validity index depending on data characteristics. Based on these inputs, we apply an optimizer from AutoML4Clust to explore the configuration space efficiently (Challenge C3). Our comprehensive evaluation shows that we can achieve more accurate results than state-of-the-art baselines. Further, we can build the meta-knowledge repository with synthetic data and still achieve accurate results on real-world data, which makes it easier to acquire training data for our learning phase. However, ML2DAC can still use complex clustering algorithms such as density-based algorithms with high runtime complexities to detect complex data characteristics (e.g., Half-Moons). (3) EffEns: To address the high runtime of complex clustering algorithms (Challenge C3), we propose an efficient ensemble clustering approach based on meta-learning. We rely on simple and efficient clustering algorithms such as k-Means to generate the ensemble and combine them using efficient consensus functions. We use meta-learning to learn which base clusterings should be used in the ensemble and which consensus function is suitable based on the data characteristics. Thus, novice analysts can also benefit from experience similar to ML2DAC to address Challenge C2. Further, we use an optimizer to tune the hyperparameters of the selected consensus function (Challenge C1). Our evaluation unveils that EffEns is much faster and scales better for large datasets than existing approaches that use complex clustering algorithms, while it can still detect complex data characteristics such as Half-Moons. We show that each approach (AutoML4Clust, ML2DAC, and EffEns) can achieve more accurate and faster results than state-of-the-art baselines. In our comprehensive overall evaluation, we compare our novel approaches theoretically and on real-world benchmark data. Our results unveil that EffEns is the only approach to address all three challenges and it achieves the most accurate and fastest results compared to AutoML4Clust and ML2DAC. Clustering is also often used in many real-world use cases for classification tasks, i.e., the data is grouped into clusters, and then separate and more specialized classification models are built on each cluster. However, it is not clear which clustering configuration should be used to achieve valuable results as different algorithms and hyperparameters are used for different use cases. Therefore, we investigate whether our novel approaches are able to find valuable clustering results that can improve classification accuracy for data with complex classification-specific characteristics. Such characteristics are multi-class imbalance and heterogeneous groups. As data with such characteristics are typically not available, we propose a synthetic data generator that can generate data with different manifestations of these characteristics and that builds the foundation for our evaluation. Our evaluation unveils that our novel approaches, in particular EffEns, can improve the classification accuracy significantly, even for less frequent classes. In conclusion, the novel approaches developed in this thesis can support novice and even more experienced analysts in achieving accurate clustering results in a short time frame.