This paper explores the design and implementation of a cognitive assistant powered by cloud computing. We begin by reviewing existing cloud service providers and assistant solutions. Building on this foundation, we define a set of requirements for our cognitive assistant and utilize them to guide the development process. To validate the functionality of our creation, we compare its outputs with established reference architectures.

The paper deals with the problematics of representation of the geometry of three-dimensional scenes in computer graphics using domain-specific hierarchical data structures based on octant trees or directed acyclic graphs as the means of space saving binary representation of this information. Several hierarchical data structures were introduced in this field in past decades. In this paper we are moving this problematic to the field of the representation of three-dimensional scenes which are rather dynamic in time, so their geometry is changing on the regular basis. We are investigating the influence of this time-variability on the size of the related hierarchical data structure on binary level. We are comparing the approach in which each time step is considered as the stand-alone static three-dimensional scene and therefore is represented by its own hierarchical data structure with the approach when this time-variable scene is considered as the four dimensional space and spatio-temporal sparse voxel octree hierarchical data structure is used for the representation of the geometry of this scene. We tested both approaches on three different scenes which were obtained by voxelization into two different resolutions of polygonal surface models represented originally as the mesh of triangles in Wavefront Object file format. Results of the tests show that spatio-temporal sparse voxel octree is better solution, when we obtained from 1.73 to 2.08 more compact binary representation, in comparison to the solution when time frames were encoded separately as the set of sparse voxel octrees.

The main objective of this research was to develop a classification model for the design of a cloud cognitive assistant. The classification model should be able to classify the textual description of the cloud architecture into desired multiple classes. For the purpose of implementation of such assistant, the analysis of current state of cognitive assistants and cloud computing was researched. As for the implementation of classifier, the overview of the possible ways how to create such classifier was also added to the research. Based on the analysis, a solution was proposed for the implementation of a model for text classification, and also a proposal for the implementation of an assistant that would use the proposed model. The Keras library was used to create a sequential text classifier. The IBM Watson cloud services were used to deploy the created model into live environment, and the services from the same group were used to develop a proposed cognitive assistant that was in the end connected to the classifier model.