Improving IS (Information System) end-user experience is one of the most important tasks in the analysis of end-users behaviour, evaluation and identification of its improvement potential. However, the application of Machine Learning methods for the UX (User Experience) usability and effic iency improvement is not widely researched. In the context of the usability analysis, the information about behaviour of end-users could be used as an input, while in the output data the focus should be made on non-trivial or difficult attention-grabbing events and scenarios. The goal of this paper is to identify which data potentially can serve as an input for Machine Learning methods (and accordingly graph theory, transformation methods, etc.), to define dependency between these data and desired output, which can help to apply Machine Learning / graph algorithms to user activity records.
Geographic Information Systems (GIS) perform the computational processing of geographic data and store the geometry and the attributes of data that are geo-referenced, that is, situated on the earth surface and represented in a cartographic projection. Integral part of GIS functionality is an opportunity to make a complex analysis, which is able to integrate geo-referenced imagery as data layers or themes and link them to other data sets producing geospatial renderings of data. Based on Service-Oriented Architecture (SOA) GIS expose its capabilities as Web services with purpose to create a flexible and extensible GIS that can quickly respond to changing and future organizational needs. In that case client’s application sends specially formed requests to the GIS services and ensures visual processing of responses. But the drawback is the count of such requests, because imaginary data and data needed for analysis are received sequentially (not simultaneously). The purpose of this paper is to present the solution of decreasing the amount of geo-requests, enabling extensions to existing GIS visual analysis tools and bringing into GIS SOA synchronization mechanism designed on principles of concatenated steganography. This ensures that various geospatial data streams will be kept, transmitted and analytically processed together using concatenated steganography, strongly taking into account mutual relations between these streams.
Day-to-day working activities have been heavily altered by COVID-19 pandemic, forcing a transition from traditional on-site work to on-line telework across the whole world. It has become much harder to efficiently organise, guide and evaluate employee’s work. There are different factors that can influence “work from home” quality, and many of these affect such work negatively. A set of relevant methods and tools should be developed which could improve this situation. The goal of the study is to summarise related background of this problem and to propose an approach to overcoming this problem. To achieve the goal, design engineer’s work is evaluated in an appropriate environment (e.g., AutoCAD, etc.) using automated analysis and visualization of IS auditing data.
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