{"id":{"repo_id":"oldenburg","oai_identifier":"oai:oops.uni-oldenburg.de:1450"},"canonical_url":"https://search.dev.ndltd.org/etd/oldenburg/oai:oops.uni-oldenburg.de:1450","repository":{"repo_id":"oldenburg","name":"Carl von Ossietzky Universität Oldenburg","base_url":"http://oops.uni-oldenburg.de/cgi/oai2"},"display":{"title":"KnoVA: A Reference Architecture for Knowledge-based Visual Analytics","abstract":"The considerably increasing Internet-based information processing leads to a rapidly increasing growth of the available data. Automatic analysis approaches are no longer sufficient for the analysis of this data. One approach to overcome this information overload phenomenon is visual analytics (VA). It combines the strengths of computers to process large amounts of data with human strengths such as flexibility, intuition, and contextual knowledge. In the process of VA experts apply knowledge. In many settings they apply similar knowledge continuously in several iterations across various tasks. Therefore a demand for concepts and methods to prevent needless repetitive analysis steps can be identified. This thesis presents KnoVA, a reference-architecture for knowledge-based visual analytics systems and evaluates it in two applications domains, automotive and healthcare. KnoVA consists of four parts: a model of the analysis process, a metadata model for knowledge-based visual analytics systems as well as concepts and algorithms for the extraction and the re-application of knowledge.","abstract_html":"The considerably increasing Internet-based information processing leads to a rapidly increasing growth of the available data. Automatic analysis approaches are no longer sufficient for the analysis of this data. One approach to overcome this information overload phenomenon is visual analytics (VA). It combines the strengths of computers to process large amounts of data with human strengths such as flexibility, intuition, and contextual knowledge. In the process of VA experts apply knowledge. In many settings they apply similar knowledge continuously in several iterations across various tasks. Therefore a demand for concepts and methods to prevent needless repetitive analysis steps can be identified. This thesis presents KnoVA, a reference-architecture for knowledge-based visual analytics systems and evaluates it in two applications domains, automotive and healthcare. KnoVA consists of four parts: a model of the analysis process, a metadata model for knowledge-based visual analytics systems as well as concepts and algorithms for the extraction and the re-application of knowledge.","abstract_has_math":false,"creators":["Flöring, Stefan"],"institution":"Universität Oldenburg","degree_name":null,"degree_level":"thesis.doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Appelrath, Hans-Jürgen","Isenberg, Tobias"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012-07-20","date_published":"2012-07-20","updated_at":"2026-07-27T20:28:14Z","subjects":["Wissensbasiertes System, Visualisierung, Analyse, Wissensextraktion, Informationsüberlastung"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://oops.uni-oldenburg.de/1450","outbound_label":"Repository record","outbound_source":"source_url"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Appelrath, Hans-Jürgen","Isenberg, Tobias"]},{"key":"dc:creator","label":"Author","values":["Flöring, Stefan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:publisher","label":"Institution","values":["BIS der Universität Oldenburg"]},{"key":"dc:type","label":"Dc Type","values":["doctoralThesis"]},{"key":"thesis:degree_level","label":"Degree Level","values":["thesis.doctoral"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Universität Oldenburg"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Wissensbasiertes System, Visualisierung, Analyse, Wissensextraktion, Informationsüberlastung"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The considerably increasing Internet-based information processing leads to a rapidly increasing growth of the available data. Automatic analysis approaches are no longer sufficient for the analysis of this data. One approach to overcome this information overload phenomenon is visual analytics (VA). It combines the strengths of computers to process large amounts of data with human strengths such as flexibility, intuition, and contextual knowledge. In the process of VA experts apply knowledge. In many settings they apply similar knowledge continuously in several iterations across various tasks. Therefore a demand for concepts and methods to prevent needless repetitive analysis steps can be identified. This thesis presents KnoVA, a reference-architecture for knowledge-based visual analytics systems and evaluates it in two applications domains, automotive and healthcare. KnoVA consists of four parts: a model of the analysis process, a metadata model for knowledge-based visual analytics systems as well as concepts and algorithms for the extraction and the re-application of knowledge.","Die seit langem deutlich zunehmende Internet-basierte Informationsverarbeitung führt zu einem rapiden Wachstum der verfügbaren Daten. Automatische Analyseverfahren sind nicht mehr ausreichend geeignet, um diese Datenbestände zu analysieren. Ein Ansatz diesem Information Overload Phänomen zu begegnen, ist die Visuelle Analyse (VA). Sie vereint Vorteile maschineller Auswertung großer Datenmengen mit menschlichen Stärken wie Flexibilität, Intuition und Hintergrundwissen. Im Prozess der VA wird Wissen durch Experten angewandt, die allerdings oft gleichartiges Wissen in mehreren Iterationen oder über verschiedene Fragestellungen hinweg wieder verwenden. Hieraus kann ein Bedarf an Konzepten und Methoden zur Verhinderung unnötig wiederholender Schritte abgeleitet werden. Als Ansatz hierzu wird in dieser Arbeit KnoVA, eine Referenzarchitektur für Wissensbasierte VA, vorgestellt und anhand von Beispielen aus den Domänen Automobilbau und Gesundheitswesen evaluiert.KnoVA besteht aus vier Teilen: einem Modell des Analyseprozesses,einem Meta-Datenmodell sowie Konzepten und Algorithmen zur Extraktion und Wiederanwendung von Wissen."]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["KnoVA: A Reference Architecture for Knowledge-based Visual Analytics","KnoVA: Eine Referenzarchitektur für Wissensbasierte Visuelle Analyse"]}]}],"canonical_facts":{"dc:contributor":["Appelrath, Hans-Jürgen","Isenberg, Tobias"],"dc:creator":["Flöring, Stefan"],"dc:description.abstract":["The considerably increasing Internet-based information processing leads to a rapidly increasing growth of the available data. Automatic analysis approaches are no longer sufficient for the analysis of this data. One approach to overcome this information overload phenomenon is visual analytics (VA). It combines the strengths of computers to process large amounts of data with human strengths such as flexibility, intuition, and contextual knowledge. In the process of VA experts apply knowledge. In many settings they apply similar knowledge continuously in several iterations across various tasks. Therefore a demand for concepts and methods to prevent needless repetitive analysis steps can be identified. This thesis presents KnoVA, a reference-architecture for knowledge-based visual analytics systems and evaluates it in two applications domains, automotive and healthcare. KnoVA consists of four parts: a model of the analysis process, a metadata model for knowledge-based visual analytics systems as well as concepts and algorithms for the extraction and the re-application of knowledge.","Die seit langem deutlich zunehmende Internet-basierte Informationsverarbeitung führt zu einem rapiden Wachstum der verfügbaren Daten. Automatische Analyseverfahren sind nicht mehr ausreichend geeignet, um diese Datenbestände zu analysieren. Ein Ansatz diesem Information Overload Phänomen zu begegnen, ist die Visuelle Analyse (VA). Sie vereint Vorteile maschineller Auswertung großer Datenmengen mit menschlichen Stärken wie Flexibilität, Intuition und Hintergrundwissen. Im Prozess der VA wird Wissen durch Experten angewandt, die allerdings oft gleichartiges Wissen in mehreren Iterationen oder über verschiedene Fragestellungen hinweg wieder verwenden. Hieraus kann ein Bedarf an Konzepten und Methoden zur Verhinderung unnötig wiederholender Schritte abgeleitet werden. Als Ansatz hierzu wird in dieser Arbeit KnoVA, eine Referenzarchitektur für Wissensbasierte VA, vorgestellt und anhand von Beispielen aus den Domänen Automobilbau und Gesundheitswesen evaluiert.KnoVA besteht aus vier Teilen: einem Modell des Analyseprozesses,einem Meta-Datenmodell sowie Konzepten und Algorithmen zur Extraktion und Wiederanwendung von Wissen."],"dc:format.medium":["application/pdf"],"dc:publisher":["BIS der Universität Oldenburg"],"dc:subject":["Wissensbasiertes System, Visualisierung, Analyse, Wissensextraktion, Informationsüberlastung"],"dc:title":["KnoVA: A Reference Architecture for Knowledge-based Visual Analytics","KnoVA: Eine Referenzarchitektur für Wissensbasierte Visuelle Analyse"],"dc:type":["doctoralThesis"],"thesis:degree_level":["thesis.doctoral"],"thesis:institution_name":["Universität Oldenburg"]},"updated_at":"2026-07-27T20:28:14Z"}