ISSN 2079-3537      

Scientific Visualization
Issue Year: 2017
Quarter: 4
Volume: 9
Number: 4
Pages: 67 - 77
Authors: K.V. Ryabinin (Russian Federation), D.A. Baranov (Russian Federation), K.I. Belousov (Russian Federation)
The paper is recommended by program committee of 27th International Conference on Computer Graphics and Vision GraphiCon’2017.
Address: K.V. Ryabinin
Perm State University, Perm, Russian Federation

D.A. Baranov
Perm State University, Perm, Russian Federation

K.I. Belousov
Perm State University, Perm, Russian Federation
Abstract: This paper is devoted to the further development of scientific visualization system SciVi. The authors created this system during the earlier research as an extensible toolset for visual analysis of arbitrary scientific data. SciVi is based on ontology engineering methods and means: its behavior is controlled by knowledge base consisting of visual objects’ ontology, data filters’ ontology and ontology of input/output statements of programming languages the external data generators (solvers) are written in. This knowledge-driven approach enables to connect SciVi to arbitrary data sources (including software/hardware solvers) and to fine-tune it for purposes of particular visualization tasks from any application domain. The user is provided with the high-level graphical interface to set up the visualization system according his/her needs. Extending SciVi with new visualization tools or data filtering mechanisms is as easy as the extending of corresponding ontologies in its knowledge base; the source code of the system’s core remains untouched.
SciVi is organized as client-server software. The server is responsible to collect the data from data source and to preprocess them if needed (the preprocessing stage may include partial rendering in case the client has insufficient performance to render the entire scene). The client provides graphical user interface and displays the final rendering result to the user.
Initially SciVi was developed as a stand-alone application with modular architecture. However sometimes it is useful to be able to incorporate individual visualization modules into third-party applications.
The reported work covers the extraction of customizable visualization module from SciVi and integration of this module with Semograph graphosematic modeling system. While the extracted rendering module is treated as an independent software library, it still remains to be a part of SciVi and therefore is driven by ontology knowledge base, which contains knowledge about graphical objects, scenes’ types and semantic filters available. This module can be easily extended and set up for arbitrary visualization tasks.
The rendering module was tested on the task of thematic text mapping visualization. The mapping has been done by informants during the interview via Internet. The mapped text is displayed as circular graph. Nodes of this graph are words ordered according to their appearance in the original text. Edges represent the semantic connections between words manifested in the number of joint words occurrences in micro-topics in the informants’ reactions. Modularity of this graph makes it possible to identify the stable micro-topics, which are typical for the certain groups of informants.
Language: Russian

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