Scalar fields occur quite commonly in several application areas in both static and time-dependent forms. Hence a proper visualization of scalar fieldsneeds to be equipped with tools to extract and focus on important features of the data. Similarity detection and pattern search techniques in scalar fields present a useful way of visualizing important features in the data. This is done by isolating these features and visualizing them independently or show all similar patterns that arise from a given search pattern. Topological features are ideal for this purpose of isolating meaningful patterns in the data set and creating intuitive feature descriptors. The Merge Tree is one such topological feature which has characteristics ideally suited for this purpose. Subtrees of merge trees segment the data into hierarchical regions which are topologically defined. This kind of feature-based segmentation is more intelligent than pure data based segmentations involving windows or bounding volumes. In this thesis, we explore several different techniques using subtrees of merge trees as features in scalar field data. Firstly, we begin with a discussion on static scalar fields and devise techniques to compare features - topologically segmented regions given by the subtrees of the merge tree - against each other. Second, we delve into time-dependent scalar fields and extend the idea of feature comparison to spatio-temporal features. In this process, we also come up with a novel approach to track features in time-dependent data considering the entire global network of likely feature associations between consecutive time steps.The highlight of this thesis is the interactivity that is enabled using these feature-based techniques by the real-time computation speed of our algorithms. Our techniques are implemented in an open-source visualization framework Inviwo and are published in several peer-reviewed conferences and journals. / <p>QC 20171020</p>

Ontologies are foundational to, and upper ontologies provide semantic integration across, the Semantic Web. Multilingualism has been shown to be a key challenge to the development of the Semantic Web, and is a particular challenge to the universality requirement of upper ontologies. Universality implies a qualitative mapping from lexical ontologies, like WordNet, to an upper ontology, such as SUMO. Are a given natural language family's core concepts currently included in an existing, accepted upper ontology? Does SUMO preserve an ontological non-bias with respect to the multilingual challenge, particularly in the context of the African languages? The approach to developing WordNets mapped to shared core concepts in the non-Indo-European language families has highlighted these challenges and this is examined in a unique new context: the Southern African languages. This is achieved through a new mapping from African language core concepts to SUMO. It is shown that SUMO has no signi ficant natural language ontology bias. / Computing / M. Sc. (Computer Science)