This paper is devoted to the development of software tools, which allow to create and analyze mental maps in digital format. Mental maps are used to study humans' cognitive characteristics and geospatial perception. To support Digital Humanities research based on the analysis of the mental maps, we developed a special high-level web-based vector graphics editor called Creative Maps Studio that allows informants to intuitively draw digital naive maps as a representation of their mental maps.The Creative Maps Studio web application provides a wide variety of drawing tools based on points, lines, and areas, which allow representing different objects on the map, such as cities, historical monuments, industrial facilities, state borders, roads, rivers, steppes, seas, swamps, etc. For each object, individual parameters can be set, such as color, size, transparency, name, description, emoticons, etc.Digital maps can be analyzed in various ways, relying on computer vision, machine learning, visualization, and manual expert analysis. Creative Maps Studio stores not only the final state of maps but also the entire history of user actions on the canvas, which makes it possible to consider the process of creating a map by the analysis.While Creative Maps Studio provides basic functions to view the informants' maps and the history of their creation, we propose involving special software to achieve a comprehensive analysis of mental maps. In particular, we rely on the previously developed SciVi data mining platform and Semograph graph-semantic modeling information system. Creative Maps Studio provides appropriate export capabilities to transfer required data to these software systems. After exporting, the expert can use SciVi and Semograph to perform visual analytics of map objects' properties, to handle maps' textual layers, and to classify the maps on the principles of the fuzzy set theory. Thus, the mentioned software can be used to carry out the comprehensive study of informants' spatial perception.In the future works, we plan to expand the set of objects available to draw the maps, allowing researchers to create custom ones, as well as to integrate analytics tools of maps creation history directly in the Creative Maps Studio.

Mental maps are valuable material for Digital Humanities research since they represent a summary of humans spatial experience reflected in their minds. Contributing to this research, we developed a high-level Web-based software platform that allows to collect drawings of mental maps and to perform corresponding data mining and fuzzy classification. The novelty of the proposed platform is the ontology-based integration of mental maps drawing engine and data mining engine, wherein all the essential steps of data mining, including data acquisition, transformation, fuzzy classification, and visual analytics are driven by ontologies. The platform consists of a high-level graphical editor to draw maps and a data flow diagram editor to define the data mining pipeline. The operators available to construct this pipeline are described by ontology, which ensures the platform’s extensibility on the knowledge base level. Thereby, the platform created can be used not only for Digital Humanities research but also for testing and evaluation of new data mining and fuzzy classification methods. Currently, we have evaluated weighted fuzzy pattern matching for mental maps fuzzy classification and confirmed the reasonable performance of this method.

The paper is devoted to the automation of visual analytics of digital mental map representation sets. Digital mental map representations are digital drawings of some certain spaces made by humans, reflecting their spatial experience and distinctive thoughts they have about the considered spatial places. The subjectivity behind mental maps fundamentally distinguishes them from the geographical maps and makes them a very fruitful material for Digital Humanities research. To unveil the potential of this research, in the previous works we developed the Creative Maps Studio vector graphics editor to enable informants intuitively draw their mental maps. In the present work, we enrich Creative Maps Studio with ontology-driven analytical subsystem to enable in-place handling of mental map representations. The proposed subsystem provides visual tools to describe the processing pipeline of mental map representations using a data flow programming paradigm wherein each processing step is described by ontology. This approach proved its flexibility and efficiency in solving different visual analytics tasks. The implemented analytical modules allow automatically render a set of mental maps representations in the graphical form and to view statistical characteristics of individual objects from these representations. The process of data preparation and visualization is described. The suggestions are proposed on the interpretation of the result. The pros and cons of using the proposed method are discussed along with the possible directions for its further development.

This paper is devoted to extending the previously created unified pipeline for conducting eye-tracking- based experiments in a virtual reality environment. In the previous work, we proposed using SciVi semantic data mining platform, Unreal Engine and HTC Vive Pro Eye head-mounted display to study reading process in the immersive virtual reality. The currently proposed extension enables to handle so-called polycode stimuli: compound visual objects, which consist of individual parts carrying different semantics for the viewer. To segment polycode stimuli extracting areas of interest (areas, where the informant’s eye gaze is being tracked) we adopt Creative Maps Studio vector graphics editor. To integrate Creative Maps Studio into the existing pipeline we created plugins for SciVi platform to load and handle the segmented stimuli, place them in the virtual reality scenes, collect corresponding eye gaze tracking data and perform visual analysis of the data collected. To analyze the eye gaze tracks, we utilize a circular graph that allows comprehensive visualization of hierarchical areas of interest (mapping them to color- coded graph nodes grouped into the hierarchy with a help of multilevel circular scale) and corresponding eye movements (mapped to the graph edges). We tested our pipeline on two different stimuli: the advertising poster and the painting “The Appearance of Christ Before the People” by A. Ivanov (1857).

In the framework of this work, an assessment method is considered and an application is proposed that implements the ability to calculate information entropy for large production systems. The importance of informational entropy for large production systems and their components allows a group of decision-makers to assess the prospect of generating managerial decisions from the perspective of completeness of knowledge about the state of the system based on available factual data.