Научная визуализация, 2020, том 12, номер 4, страницы 56 - 70, DOI: 10.26583/sv.12.4.06
Novel Circular Graph Capabilities for Comprehensive Visual Analytics of Interconnected Data in Digital Humanities
Авторы: K.V. Ryabinin1, K.I. Belousov2, S.I. Chuprina3
Perm State University, Perm, Russia
1 ORCID: 0000-0002-8353-7641, kostya.ryabinin@gmail.com
2 ORCID: 0000-0003-4447-1288, belousovki@gmail.com
3 ORCID: 0000-0002-2103-3771, chuprinas@inbox.ru
Аннотация
The paper is devoted to the development of tools, which enable to improve the comprehensive power of visual analytics of interconnected data. This kind of data is a great challenge for researchers in the field of Digital Humanities. We propose using ontology-driven SciVi visual analytics platform to tackle this challenge and help researchers to bring data to life.
The proposed analytics components are based on the circular graph, representing the data elements as the circle distributed nodes and the data elements’ connections as the cubic parabolas’ arcs. SciVi platform provides not only the traditional interactive means for graph visual analytics, such as node searching based on regular expressions, highlighting of incident edges and connected nodes by mouse hover, depicting clusters by colors, threshold-based filtering of weighted nodes and edges, etc., but also a set of new features, which help to solve special analytics tasks. The paper presents these novel features and corresponding use cases.
First, we propose an ontology-driven data extraction, transformation and loading mechanism that allows obtaining the input data from different sources and preprocessing them by custom algorithms defined by means of high-level visual programming language. Second, we developed a multilevel ring scale that is placed around the circular graph allowing to group the graph nodes according to the given classifier and automatically reorder them at runtime. Third, we demonstrate an implementation of the equalizing filter that allows applying different filtering thresholds to different groups of graph nodes/edges to cut off the noisy data. This is necessary for data wrangling in the case the data noise has a non-uniform strength distribution across the graph. Fourth, we developed a graph state calculator that allows data comparison by performing different operations like union, intersection, etc. on the data slices shown within the graph. Fifth, we make it possible to synchronize the data slice currently visualized by the graph with the corresponding localized area on the geographical map. Thanks to the features presented, the SciVi advanced interactive tools can harness the power of visual analytics in Digital Humanities and Big Data.
Ключевые слова: Visual Analytics, Circular Graph, Geographical Map, Data Filtering, Data Comparison, Ontology Engineering, Digital Humanities.
