2013. No. 4(26)

Fuad Aleskerov  – Head of Department of Mathematics, Faculty of Economics, National Research University Higher School of Economics; head of laboratory, Trapeznikov Institute of Control Sciences, Russian Academy of Sciences.
Address: 20, Myasnitskaya str., Moscow, 101000, Russian Federation.
E-mail: alesk@hse.ru

In this paper the term ‘pattern’ is referred to a combination of values of some features such that these feature values define a group of objects that ​​significantly differ from other objects. In the paper’s first part, we presented a review of the literature on each of the three main aspects of this concept: (1) examples of usage of the concept of ‘pattern’ in sciences and technology, (2) methods of cluster analysis, with an eye over those relevant, and (3) dynamics of multi-dimensional objects. Then we proposed three different, but mathematically equivalent, definitions of the concept of pattern using (a) parallel coordinates, (b) conjunctive descriptions, and (c) geometric ‘boxes’.
In this part we give a representative sample of examples of static and dynamic analysis of patterns, within the most common framework of parallel coordinates. The static pattern analysis is a two-step method of the automated pattern formation. At the first stage we use classical cluster analysis to find clusters of objects, and at the second stage we find patterns that adequately represent the obtained clusters. Dynamic analysis of patterns is to highlight the types of functional stability of objects depending on how frequently an object changes between the patterns to which it belongs at different time periods. This typology may help in managing objects; also, it allows us to determine groups of risk consisting of objects changing too frequently.
Specifically, we build and demonstrate patterns in data for: (a) comparative macroeconomic analysis, (b) evaluation of efficiency and analysis of business models attended to by banks in Turkey and Russia, (c) patterns of voting behavior and electoral change in General Elections in the UK and Municipal Elections in Finland, and (d) issues of innovation in the development of regions in the Russian Federation in the long run. This review outlines a number of research projects at which authors participated. Some of these projects had been conducted before the concept of pattern was developed in full. In this sense the current paper generalizes those earlier publications.

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.

Sergey Glushenko – Assistant Professor, Department of Economic Informatics and Automation of Management, Rostov State Economic University.  
Address: 69, Bolshaya Sadovaya str., Rostov-on-Don, 344002, Russian Federation.
E-mail: www.555.sergey@mail.ru

The article explains the importance of the application of risk analysis in the management system of information security (IS), and studies the most widespread methods of risk assessment NIST and CRAMM, as well as settles the limitations and drawbacks of these approaches. Risk assessment of a company’s information security is proposed to be carried out by using the theory of fuzzy logic. Application of fuzzy models allows taking into account both quantitative and qualitative characteristics, as well as represent fuzzy descriptions using fuzzy sets and linguistic variables. The proposed methodology has been the basis for developing a fuzzy production model (FPM), which identifies seven input linguistic variables characterizing risk factors, and four output linguistic variables characterizing the risks of different areas of information security. The model contains four rule bases and allows linguistic analysis of information security risks of the organization. FPM allows removing restrictions on the number of input variables taken into account and integrating both qualitative and quantitative approaches to risk assessment.
Implementation of the rule base fuzzy modeling process is carried out by applying specialized package Fuzzy Logic Toolbox from software MATLAB. The mechanism for obtaining risk assessments based on the Mamdani algorithm allows obtaining the numerical value of risk, linguistic description of risk, as well as expert’s level of confidence in the occurrence of a risk event. The simulation results can be used by IT- managers for identifying risks priorities (very high, high, medium, low, very low), and selecting an action plan to reduce the impact of the most dangerous threats to the organization’s information security. 

George Kukharev– Professor, Department of Software and Application of Computers, Faculty of Computer Technologies and Informatics, Saint Petersburg Electrotechnical University; Professor, West Pomeranian University of Technology, Szczecin, Poland.
Address: 5, Professora Popova str., St. Petersburg, 197376, Russian Federation.
E-mail: kuga41@gmail.ru, gkukharev@wi.zut.edu.pl

Ekaterina Kamenskaya– Software Engineer, Google Zürich, Switzerland.  
Addrress: 110, Brandschenkestrasse, Zürich, 8002, Switzerland.
E-mail: ekamenskaya@google.com

Nadezhda Shchegoleva – Associate Professor, Department of Software and Application of Computers, Faculty of Computer Technologies and Informatics, Saint Petersburg Electrotechnical University.  
Address: 5, Professora Popova str., St. Petersburg, 197376, Russian Federation.
E-mail: stil_hope@mail.ru

This paper discusses the methods of presentation and comparison for semantically unrelated images, visually similar (that is having similar color, shape, texture), and assessment of their similarity in the original feature space, and in Canonical Variables Space (CVS). The projection of the source images in CVS is implemented using two-dimensional canonical Canonical Correlation Analysis – 2D CCA/2D KLT presented in this paper, and the measure of their similarity in CVS is based on the phase correlation.
To compare images in the original feature space, we used color brightness histograms and mutual phase correlation between histograms, mutual phase correlation between images, Structural SIMilarity Index (SSIM). However, we could only partially prove similarity corresponding to subjective comparison of selected images - the presence of phase correlation between the color brightness histograms, that is based on the similarity of images colors. Mutual phase correlation between images, as well as structural similarity index showed no images similarity.
The projection in the space of canonical variables is implemented using 2D CCA/2D KLT, specifically designed to handle two sets of images and presented in detail in this paper. This allowed confirming the fact of the correlation between the images of the dog and its owner in the space of canonical variables, while other ways failed to confirm it. It is shown that what is «dissimilar» in the original feature space may be similar in the space of canonical variables. This allows indexing some images through other by using 2D CCA methods (search, recognition, model mapping of one image to another, reconstruction of images). The results prove that 2D CCA/2D KLT methods can be widely used in search, pattern recognition and image classification tasks, and to decrease redundancy of images representation regardless of their semantic relationships.

Sergey Soloviev– Professor, Department of Programming Languages, Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University.
Address: 1, build. 52, Lomonosov Moscow State University, Leninskie Gory, GSP-1, Moscow, 119991, Russian Federation.
E-mail: soloviev@glossary.ru

Daria Stelmashenko – Engineer, Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University.
Address: 1, build. 52, Lomonosov Moscow State University, Leninskie Gory, GSP-1, Moscow, 119991, Russian Federation.
E-mail: dashanikolaeva@gmail.com

The article looks into the problem of merging local contexts, describe the same global set of objects in the terms of different sets of attributes. The resulting solution of this problem is a formal concept lattice that is defined on a global set of objects via the unified set of attributes. Among the known methods for solving this problem, the @FC method has been chosen, which allows to precisely building the desired lattice. It is pointed out that the @FC method brings the solution of the problem to a sequence of formally-grounded classification solutions, on the basis of the partially given descriptions of objects. The complexity of solving the problem with @FC method is in the direct dependence from the number of successful classification decisions, which brings in the necessity of expanding their range. In similar conditions, an expert classification method has already performed as a rather effective instrument that allows transferring the once obtained classification decisions onto other objects. The base of the expert classification method is formed by the domination relation on objects descriptions, which is obtained during the dialog with an expert. The fundamental difficulty in incorporating of expert classification method into the @FC method is in the necessity of a self-consistent extrapolation of the domination relation on partial descriptions of objects from the global set. The article describes a concrete method for extrapolating the domination relation, which eliminates this difficulty and allows combining of these two methods. The immediate practical result of this research is the development of an effective modification of the context merging method. From the theoretical point of view, the proposed way of combining the two methods generates a number of interesting research topics.

Arkadiy Maron – Associate Professor, Department of Business Analytics, Faculty of Business Informatics, National Research University Higher School of Economics.  
Address: 20, Myasnitskaya str., Moscow, 101000, Russian Federation.
E-mail: amaron@hse.ru

Currently, many leading industrial Research Institute’s (RI) are subsidiaries of the monopoly sector corporate entities, which are the main customers of their works. Their order will be called corporate order here. Any Corporate order must be performed. If, for this purpose a RI possesses workforce to perform the work under contracts with other customers, then, being a commercial structure, they are obliged to do so. This arises an issue is how to distribute the available workforce.

In a competitive market, regardless of whether the corporate order is approved or not, there is uncertainty about the amount of work that can be obtained from third-party customers. However, the process of drawing agreements with third-party customers should begin in advance. There can arise the following risks: too many agreements concluded leaves not enough resources for the Corporate order; on the other hand, few contracts concluded allows reserving the excess manpower to carry out the Corporate order. The first of these risks is critical and thusly should be avoided. Running the second risk would lead to the fact that a certain number of employees will not be drawn to the fulfillment of contracts, which will naturally reduce profits.

To solve this problem, we offer to apply a scenario approach. For each scenario, the task of distribution of workforce can be reduced to a linear programming task. After this, to achieve optimal allocation, we use optimization method based on Wald criterion. The payouts table will consist of the values of benefits derived by solving linear programming tasks corresponding to different scenarios.

The proposed approach allows solving the task of securing resources for corporate orders at a time when it has not yet been approved. This enables the RI management to carry out contractual business with commercial customers, without compromising the guaranteed execution of works commissioned by the principal founder.

Nerses Khachatryan – Senior Researcher, Central Economics and Mathematics Institute, Russian Academy of Sciences; Senior Lecturer, Department of Business Analytics, Faculty of Business Informatics, National Research University Higher School of Economics.  
Address: 47, Nakhimovskiy Prospect, Moscow, 117418, Russian Federation.
E-mail: nerses@cemi.rssi.ru

This article studies the model of the organizing cargo transportation in a closed chain of stations with a given control system. It is assumed that there is an open-line track between two neighboring stations in which temporarily part of the cargo may be stored. Cargo can be delivered on any station either from the previous station or from the open-line track located between them. Similarly, cargo may be sent either to the next station or to the open-line track between them. Cargo handling occurs at nodes stations. Obviously, the number of nodes involved in the cargo handling under uninterrupted operation of the entire transport chain is limited. The maximum number of such nodes determines the carrying capacity of stations. Capacity of the open-line track is limited. Cargo transportation is fulfilled by means of two technologies. The first technology is based on a set of normative rules of interaction between neighboring stations. It does not take in to account conditions of limited carrying capacity of stations. Furthermore, it does not allow using the full potential of stations. In this regard, along with the first technology, the second technology is used. It allows both to increase the number of involved nodes, and to reduce it. In this case, the cargo is either taken from an open-line track or sent to an open-line track. This model is determined by a system of differential equations describing the intensity of cargo traffic at the stations, which satisfies the additional conditions. These conditions determine the control system and impose restrictions on the capacity of open-line tracks. Particularly interesting is the study of stationary solutions in this system. This system has three types of stationary solutions. The main objective is to study stability of stationary solutions. Analytical study of the set of all solutions is greatly complicated by the fact that the right-hand sides of the differential equations are not continuous functions. Therefore, this model has been studied numerically. Numerical study has shown that only one type of stationary solutions is stable. For these solutions the domain of their stability is described.

Pavel Oleynik – Associate Professor, System Software Architect, Aston JSC.
Address: 80, Bugrovatyi Pereulok, Shakhty, Rostov region, 346519, Russian Federation.
E-mail: xsl@list.ru

The article represents the major structural elements of unified environment of information systems rapid development for various domains. It describes the structure of the data access layer, the methods of applications design and implemented projects. As the basis, we have taken modern architecture MDA (Model Driven Architecture), and defined the criteria for the optimal development of the built environment, called SharpArchitect RAD Studio. The major criteria of optimality are the following: the ability to describe any application domains; the possibility of implementing different architectures of applications, from classical two-tier "file server" to the multi-tier architecture with support for "distributed objects"; the possibility of using an up-to-date object-oriented programming language and a modern relational database management system. Among the criteria also are: functional support for access rights to objects based on user accounts; implementation of the plug-in architecture, involving the creation of extensions functional modules of the system.

The article presents a Metamodel class diagram of the object system, which allows describing all of the major structural elements of the object-oriented paradigm. Before implementing the Metamodel, the following optimality criteria have been put forward: (1) Metamodel must be unified; (2) a set of core system classes must be provided that implements the most common functionality; (3) an extensive hierarchy of atomic literal types must be provided representing the most common types of data for modern object-oriented programming languages.

When implementing the Metamodel, we used the best approaches of those existing. The hierarchy of the literal type is based on the solutions described in the standard object-oriented database ODMG 3.0 and SQL:2003 standard regulating the object extensions of relational databases. The implemented development environment uses three main methods (patterns) of object-relational mapping, namely: Single Table Inheritance, Class Table Inheritance, Concrete Table Inheritance. These patterns are applied to organizing the data access level. To assess the system, we describe the qualitative and quantitative indicators of completed projects. The environment has been used to design 12 degree projects, as well as the information system for the scientific conferences SharpArchitect Scientific Conference Manager (for conference "Object systems") and a platform for creating and managing stock exchange trading robots.