Visualization is an important tool for presenting data to humans in an easy-to-understand manner. With new radar technology in development that can gather 3D atmosphere data, it opens up new possibilities for using 3D visualization tools to visualize the data, e.g, OpenSpace. OpenSpace is an open-source tool for visualization of the cosmos and universe. An evaluation of different rendering methods inside OpenSpace is evaluated to answer which method is most suitable for visualizing atmospheric 3D data. The data is in the format of HDF5 files and contains a list of beams with samples scattered along the beams, an algorithm is implemented to transform the beam data into a 3D volume which is used inside OpenSpace to be rendered. Tests are implemented to gather information on which parameter in the algorithm affects CPU execution time the most. The tests consist of executing the algorithm 100 times with different combinations of parameters to see which parameter has the largest effect on execution time, and a complexity analysis is calculated to evaluate the complexity of the algorithm. The results of the tests shows that the height of the volume affects execution performance the most on larger sizes. On small sizes, the difference between the different dimensions are insignificant. With the combination of height and smoothing, it slowed the execution time by a larger margin compared to width and smoothing or depth and smoothing. By implementing Volumetric ray casting and Point clouds as rendering methods, the results showed that both can visualize the data in real-time. Volumetric ray casting rendered with a clearer result in comparison to Point clouds, thus, Volumetric ray-casting is the preferred method to use when rendering atmospheric 3D volumes that is able to meet certain criteria.

In Description Logics (DLs), both tableau-based and automatabased algorithms are frequently used to show decidability and complexity results for basic inference problems such as satisfiability of concepts. Whereas tableau-based algorithms usually yield worst-case optimal algorithms in the case of PSpace-complete logics, it is often very hard to design optimal tableau-based algorithms for ExpTime-complete DLs. In contrast, the automata-based approach is usually well-suited to prove ExpTime upper-bounds, but its direct application will usually also yield an ExpTime-algorithm for a PSpace-complete logic since the (tree) automaton constructed for a given concept is usually exponentially large. In the present paper, we formulate conditions under which an on-the-fly construction of such an exponentially large automaton can be used to obtain a PSpace-algorithm. We illustrate the usefulness of this approach by proving a new PSpace upper-bound for satisfiability of concepts w.r.t. acyclic terminologies in the DL SI, which extends the basic DL ALC with transitive and inverse roles.

Big Data and Big Data analytics have attracted major interest in research and industry and continue to do so. The high demand for capable and scalable analytics in combination with the ever increasing number and volume of application scenarios and data has lead to a large and intransparent landscape full of versions, variants and individual algorithms. As this zoo of methods lacks a systematic way of description, understanding is almost impossible which severely hinders effective application and efficient development of analytic algorithms. To solve this issue we propose our concept of modular analytics that abstracts the essentials of an analytic domain and turns them into a set of universal building blocks. As arbitrary algorithms can be created from the same set of blocks, understanding is eased and development benefits from reusability.