Architecture of geo-information resource for forming an information-analytical environment analytics

Abstract

The work is devoted to solving a scientific and practical problem, which consists in developing the architecture of a geoinformation resource for the formation of an information-analytical environment for an analyst. The purpose of the research is to increase the efficiency of information-analytical work by using approaches to building a geoinformation resource, forming an information space based on the results of geospatial intelligence. Basic research indicates a steady trend towards an increase in the volume of data and the frequency of their receipt from different sources, which requires the use of new methods of their collection and processing. The processing of each type of information is constantly being improved and organized using automation tools. However, the analyst's ability to simultaneously process the entire data set and conduct its analysis remains limited. The modern requirements for the efficiency of information processing and the reduction of terms for its provision, this issue is very relevant, therefore, the search for new approaches in this direction is an important development process. More than 80% of information has a spatial reference, it is advisable to use a geoinformation approach for the complex processing and analysis of the obtained intelligence information. Analysis of existing solutions shows that the requirements are met to some extent. However, the existing functionality needs to be improved, and some creation, due to its absence.
The use of semi-automated (automated) methods for processing textual documents improves the efficiency of an analyst’s work, as it eliminates the need to read through all incoming materials and allows them to focus solely on performing specific tasks. The implemented algorithms for semi-automated (automated) processing of textual materials enable their handling directly at the point of information extraction, which significantly reduces the time required for search and processing.
The results of the study will help increase the efficiency of the analyst's work, namely, reduce the time spent on data preparation, and provide the opportunity to focus on their processing and obtaining a more accurate result (conclusion, forecast).