Browsing by Author "Ziegler, Julian"

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    Berechnung und Darstellung der Verwendungshäufigkeiten von programmiersprachlichen Konzepten
    (2014) Ziegler, Julian
    Da die Code-Basis von heutiger Software einem zunehmendem Wachstum unterworfen ist, entfällt ein immer größerer Teil der Zeit für Wartung und Erweiterung an das Verstehen des zugrunde liegenden Codes. Hierbei können Tools Abhilfe schaffen, in dem sie auf dem vorliegenden Code bestimmte Analysen durchführen. In dieser Arbeit soll ein Programm entwickelt werden, welches die Verwendungshäufigkeiten programmiersprachlicher Konzepte berechnet und visuell darstellt. Diese Analyse geschieht auf Basis der Bauhaus Zwischensprache IML und soll neben dem eigentlichen Programm auch solche Konzepte mit einbeziehen, die von eingebundenen Bibliotheken zur Verfügung gestellt werden.
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    Metadata management in virtual product development to enable cross-organizational data analytics
    (2024) Ziegler, Julian; Mitschang, Bernhard (Prof. Dr.-Ing. habil.)
    Due to the advancing digitalization, companies are increasingly adopting computer-aided technologies. Especially in product development, computer-aided technologies enable a gradual shift from physical to virtual prototypes. This shift towards virtual product development includes design, simulation, testing, and optimization of products, and reduces costs and time needed for these tasks. Companies with strong activities in the field of virtual product development generate large amounts of heterogeneous data and wish to mine these data for knowledge. In this context, metadata is a key enabler for data discovery, data exploration, and data analyses but often neglected. The diversity in the structure and formats of virtual product development data makes it difficult for domain experts to analyze them. Domain experts struggle with this task because such engineering data are not sufficiently described with metadata. Moreover, data in companies are often isolated in data silos and difficult to explore by domain experts. This calls for an adequate data and metadata management that is able to cope with the significant data heterogeneity in virtual product development, and that empowers domain experts to discover and access data for further analyses. This thesis identifies previously unsolved challenges for a data and metadata management that is tailored to virtual product development and makes three contributions. First, a metadata model that provides a connected view on all data, metadata, and work activities of virtual product development projects. A prototypical implementation of this metadata model is already being applied to a real-world use case of an industry partner. Based on this foundation, the second contribution uses this metadata model to enable feature engineering with domain experts as part of data analyses projects. Going further, data analyses can directly use the metadata structure to provide added value without having to access the large amounts of product data. To this end, the third contribution utilizes the metadata structure itself to enable a novel approach to process discovery for product development projects. Thus, process structures in development projects can be analyzed with little effort, e.g., to identify good or inefficient processes in development projects.
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    A security concept for distributed data processing systems
    (2017) Ziegler, Julian
    Today, the amount of raw data available is abundant. As only a small part of this data is in a form fit for further processing, there is many data left to analyze and process. At the same time, cloud services are ubiquitous and allow even small businesses to perform large tasks of distributed data processing without the significant costs required for a suitable computational infrastructure. However, as more and more users transfer their data into the cloud for processing and storage, concerns about data security arise. An extensive review of data security research in today's cloud solutions confirms these concerns to be justified. The existing strategies for securing one's data are not adequate for many use cases. Therefore, this work proposes a holistic security concept for distributed data processing in the cloud. For the purpose of providing security in heterogeneous cloud environments, it statically analyzes a data flow prior to execution and determines the optimal security measurements. Without imposing strict requirements on the cloud services involved, it can be deployed in a broad range of scenarios. The concept's generic design can be adopted by existing data rocessing tools. An exemplary implementation is provided for the mashup tool FlexMash. Requirements, such as data confidentiality, integrity, access control, and scalability were evaluated to be met.