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ISSN 2587-814X (print),
ISSN 2587-8158 (online)

Russian version: ISSN 1998-0663 (print),
ISSN 2587-8166 (online)

Tatiana Gavrilova 1, Dmitry Kudryavtsev , Irina Leshcheva , Yaroslav Pavlov 
  • 1 Saint-Petersburg University, 3, Volkhovsky Pereulok, St. Petersburg, 199004, Russian Federation

On a method of visual models classification

2013. No. 4(26). P. 21–34 [issue contents]

Tatiana Gavrilova – Professor, Head of Department of IT in Management, Graduate School of Management, St. Petersburg State University.
Address: 3, Volkhovsky Pereulok, St. Petersburg, 199004, Russian Federation.
E-mail: gavrilova@gsom.pu.ru

Dmitry Kudryavtsev – Head of Knowledge Management Practice, Business Engineering Group – St. Petersburg; Associate Professor, St. Petersburg State Polytechnic University
Address: office 237, 3 lit. K, Rozhdestvensky Business Center, Furazhnyi Pereulok, St. Petersburg, 191015, Russian Federation.
E-mail: dmitry.ku@gmail.com 

Irina Leshcheva – Senior Lecturer, Department of IT in Management, Graduate School of Management, St. Petersburg State University.
Address: 3, Volkhovsky Pereulok, St. Petersburg, 199004, Russian Federation.
E-mail: leshcheva@gsom.pu.eu 

Yaroslav Pavlov – Junior Researcher, Graduate School of Management, St. Petersburg State University.
Address: 3, Volkhovsky Pereulok, St. Petersburg, 199004, Russian Federation.
E-mail: yaroslav.pavlov@gmail.com 

Today more and more specialists and managers are coming to understand the importance of structuring and visualization of knowledge. Visualization allows graphical representation of processes, events and concepts in those cases when their immediate perception is not possible. Graphical representation as a method of compact organization of information can be used as a powerful method of thinking applicable to all spheres of intellectual activities including modeling enterprises, complex organizational structures, as well as business learning processes. Visualization is vitally important in enhancing learning both in traditional and distant modes. Visual representation of knowledge contributes to understanding and communication between the teacher and the student. Although the number of visual languages is not very large, in many cases their choice is rather random and is not based on strict features. This is based mainly on the lack of a clear classification of visualization tools and the absence of recommendations for choosing a graphic model.
The objective of this work is to design and develop a new taxonomy of visual languages allowing a better choice depending of the type of knowledge they represent. The suggested approach is based on the semantic classification of visual languages and types of knowledge.
The research produces a new classification of visual modeling languages, which allows choosing a visualization technique depending on the type of knowledge to be represented. The article also provides the description of knowledge types by means of questions testing the competence; it also defines the basic concepts and relations featuring each knowledge type and their corresponding languages. On the one hand, the results of the description of competence testing questions verify the classification of visual languages, and, on the other hand, they help understand which knowledge type is in need of visualization. All the above-mentioned classifications and descriptions are integrated into the method of selecting a visual modeling language. Besides, the article identifies the limitations of visual languages and proposes the improvements: integration with the ontological engineering, methods engineering and the best industry practices, as well as non-graphical modeling tools.

Citation: (2013) Ob odnom metode klassifikatsii vizual'nykh modeley [On a method of visual models classification] Biznes-informatika, 4(26) (in Russian)
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