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 Efficient exploratory clustering analyses in large-scale exploration processes(2021) Fritz, Manuel; Behringer, Michael; Tschechlov, Dennis; Schwarz, HolgerClustering is a fundamental primitive in manifold applications. In order to achieve valuable results in exploratory clustering analyses, parameters of the clustering algorithm have to be set appropriately, which is a tremendous pitfall. We observe multiple challenges for large-scale exploration processes. On the one hand, they require specific methods to efficiently explore large parameter search spaces. On the other hand, they often exhibit large runtimes, in particular when large datasets are analyzed using clustering algorithms with super-polynomial runtimes, which repeatedly need to be executed within exploratory clustering analyses. We address these challenges as follows: First, we present LOG-Means and show that it provides estimates for the number of clusters in sublinear time regarding the defined search space, i.e., provably requiring less executions of a clustering algorithm than existing methods. Second, we demonstrate how to exploit fundamental characteristics of exploratory clustering analyses in order to significantly accelerate the (repetitive) execution of clustering algorithms on large datasets. Third, we show how these challenges can be tackled at the same time. To the best of our knowledge, this is the first work which simultaneously addresses the above-mentioned challenges. In our comprehensive evaluation, we unveil that our proposed methods significantly outperform state-of-the-art methods, thus especially supporting novice analysts for exploratory clustering analyses in large-scale exploration processes.Item Open Access Introducing the enterprise data marketplace : a platform for democratizing company data(2023) Eichler, Rebecca; Gröger, Christoph; Hoos, Eva; Stach, Christoph; Schwarz, Holger; Mitschang, BernhardIn this big data era, multitudes of data are generated and collected which contain the potential to gain new insights, e.g., for enhancing business models. To leverage this potential through, e.g., data science and analytics projects, the data must be made available. In this context, data marketplaces are used as platforms to facilitate the exchange and thus, the provisioning of data and data-related services. Data marketplaces are mainly studied for the exchange of data between organizations, i.e., as external data marketplaces. Yet, the data collected within a company also has the potential to provide valuable insights for this same company, for instance to optimize business processes. Studies indicate, however, that a significant amount of data within companies remains unused. In this sense, it is proposed to employ an Enterprise Data Marketplace, a platform to democratize data within a company among its employees. Specifics of the Enterprise Data Marketplace, how it can be implemented or how it makes data available throughout a variety of systems like data lakes has not been investigated in literature so far. Therefore, we present the characteristics and requirements of this kind of marketplace. We also distinguish it from other tools like data catalogs, provide a platform architecture and highlight how it integrates with the company’s system landscape. The presented concepts are demonstrated through an Enterprise Data Marketplace prototype and an experiment reveals that this marketplace significantly improves the data consumer workflows in terms of efficiency and complexity. This paper is based on several interdisciplinary works combining comprehensive research with practical experience from an industrial perspective. We therefore present the Enterprise Data Marketplace as a distinct marketplace type and provide the basis for establishing it within a company.Item Open Access Exploiting domain knowledge to address class imbalance and a heterogeneous feature space in multi-class classification(2023) Hirsch, Vitali; Reimann, Peter; Treder-Tschechlov, Dennis; Schwarz, Holger; Mitschang, BernhardReal-world data of multi-class classification tasks often show complex data characteristics that lead to a reduced classification performance. Major analytical challenges are a high degree of multi-class imbalance within data and a heterogeneous feature space, which increases the number and complexity of class patterns. Existing solutions to classification or data pre-processing only address one of these two challenges in isolation. We propose a novel classification approach that explicitly addresses both challenges of multi-class imbalance and heterogeneous feature space together. As main contribution, this approach exploits domain knowledge in terms of a taxonomy to systematically prepare the training data. Based on an experimental evaluation on both real-world data and several synthetically generated data sets, we show that our approach outperforms any other classification technique in terms of accuracy. Furthermore, it entails considerable practical benefits in real-world use cases, e.g., it reduces rework required in the area of product quality control.Item Open Access The lakehouse : state of the art on concepts and technologies(2024) Schneider, Jan; Gröger, Christoph; Lutsch, Arnold; Schwarz, Holger; Mitschang, BernhardIn the context of data analytics, so-called lakehouses refer to novel variants of data platforms that attempt to combine characteristics of data warehouses and data lakes. In this way, lakehouses promise to simplify enterprise analytics architectures, which often suffer from high operational costs, slow analytical processes and further shortcomings resulting from data replication. However, different views and notions on the lakehouse paradigm exist, which are commonly driven by individual technologies and varying analytical use cases. Therefore, it remains unclear what challenges lakehouses address, how they can be characterized and which technologies can be leveraged to implement them. This paper addresses these issues by providing an extensive overview of concepts and technologies that are related to the lakehouse paradigm and by outlining lakehouses as a distinct architectural approach for data platforms. Concepts and technologies from literature with regard to lakehouses are discussed, based on which a conceptual foundation for lakehouses is established. In addition, several popular technologies are evaluated regarding their suitability for the building of lakehouses. All findings are supported and demonstrated with the help of a representative analytics scenario. Typical challenges of conventional data platforms are identified, a new, sharper definition for lakehouses is proposed and technical requirements for lakehouses are derived. As part of an evaluation, these requirements are applied to several popular technologies, of which frameworks for data lakes turn out to be particularly helpful for the construction of lakehouses. Our work provides an overview of the state of the art and a conceptual foundation for the lakehouse paradigm, which can support future research.