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This paper is grounded in the emerging field of web science and shall contribute to its further classification and demarcation by illustrating the current state of »web-native research methods«. It builds upon an initial arraying work of Richard Rogers, who coined the term »Digital Methods« for research with methods that were »born« in the web, and illustrated and organized them in his eponymous book in 2013. This paper attempts to develop a more appropriate illustration of the Digital Methods by following the web’s very own, hypertextual, network-like nature, in particular by construing an ontological representation on the base of the Web Ontology Language (OWL). By virtue of decomposing the book into granular information units and their subsequent reassembly into OWL entities, immediate access to the entire knowledge domain can be provided, and coherencies, interrelations and distinctions between concepts become apparent. The ontology’s structure was induced narrowly along the provided examples of research projects and subsequently clustered in topic groups, of which the three most important ones were (a) the Digital Methods as an arraying space of web-native methodology, (b) a collection of concrete applications of these Digital Methods in research projects, and (c) a hierarchical scheme of traditional sciences with a distinct interest in answering research questions with help of Digital Methods. Subsequently, the ontology was evaluated in three general dimensions: Deriving user stories and scenarios provided means to validate the utilization quality; the accuracy and reliability of the resulting structure was validated with help of a control group of web-native research projects; and process control instruments served as a validator for the ontology’s correctness. Despite the ontology itself, this paper also resulted in a first interpretation of the produced information: Statements about research practise in social science, politics and philosophy were as possible as findings about commonly applied varieties of methods. Concluding, the present paper proposes a process of ontology engineering, an evaluation of the ontology’s value, and an interpretation of the ontology’s content.
The amount of data produced and stored in multiple types of distributed data sources is growing steadily. A crucial factor that determines whether data can be analyzed efficiently is the use of adequate visualizations. Almost simultaneously with the ongoing availability of data numerous types of visualization techniques have emerged. Since ordinary business intelligence users typically lack expert visualization knowledge, the selection and creation of visualizations can be a very time- and knowledge-consuming task. To encounter these problems an architecture that aims at supporting ordinary BI users in the selection of adequate visualizations is developed in this thesis. The basic idea is to automatically provide visualization recommendations based on the concrete BI scenario and formalized visualization knowledge. Ontologies that formalize all relevant knowledge play an important role in the developed architecture and are the key to make the knowledge machine-processable.