2013. No. 3(25)

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

Veronika Belousova – Head of Budgeting Methodology Department, Institute for Statistical Studies and Economics of Knowledge, National Research University Higher School of Economics.
Address: 20, Myasnitskaya str., Moscow, 101000, Russian Federation.
E-mail: vbelousova@hse.ru

Ludmila Egorova – Lecturer, Department of Mathematics, Faculty of Economics, National Research University Higher School of Economics.
Address: 20, Myasnitskaya str., Moscow, 101000, Russian Federation.
E-mail: legorova@hse.ru

Boris Mirkin – Professor, Department of Data Analysis and Artificial Intelligence, School of Applied Mathematics and Information Science, National Research University Higher School of Economics.
Address: 20, Myasnitskaya str., Moscow, 101000, Russian Federation.
E-mail: bmirkin@hse.ru

In this paper the term “pattern” refers to a combination of values of some features such that objects with these feature values ​​significantly differ from other objects. Although some works related to finding and using patterns have been published in the literature, this paper probably is the first to consider the concept as a general tool for the analysis of behavior of objects in both statics and dynamics.

The concept of “pattern” is defined here in three equivalent mathematical frameworks that appeal to different cognitive subsystems pertaining to image, logics and geometry, respectively. The first approach utilizes parallel coordinates for the visual analysis in order to determine different patterns, the second approach uses conjunctive interval predicates that define a set of classifiers separating the patterns from each other and from the rest, and the third approach represents a pattern as the Cartesian product of the corresponding intervals.

The paper proposes a two-stage method for automation of the process of patterns formation. At the first stage we use classical cluster analysis to find clusters of objects; at the second stage we find patterns that adequately represent the obtained clusters. If the data describes functioning of various socio-economic objects in time, we add the third stage at which we analyze pattern changing behavior of the objects. Objects with a stable pattern over time are of a special interest because they can represent objects well adapted to their environment.

We present a review of the literature on each of the three stages: a review of cluster analysis methods, examples of the usage of the term “pattern” in various subject areas of science as a template data structure, and the dynamics of multi-dimensional objects through the examples from several theoretical and practical works similar to the dynamic pattern analysis.

Igor Bolshakov – Professor, Independent Researcher, Honored Professor of National Polytechnic Institute, Mexico.
Address: 45, Lesnaya str., Moscow, 127055, Russian Federation.
E-mail: iabolshakov@gmail.com

Over the last 25 years the Russian language has undergone some significant changes. Colloquial and slang words, accumulated by the society, started to appear on pages of magazines and newspapers, in advertisements, on TV screens and in the Internet. There are also many new borrowed words and some words have even changed their meanings. So, the list of the Russian collocations has also changed and enlarged. Therefore it has become of paramount importance to compose dictionaries which will reflect approved and disapproved of links between words.

This paper discusses issues of developing a network structure and cross-lexical principles for a new type of dictionary, unique in its structure and volume. The principles of network structure intends for every glossary element to be accompanied by its own links. The content of cross-lexical array is based on paper dictionaries, web news and analytics, as well as various advertisements and magazines (about celebrities, fashion, tourism and automobile industry). Topics include economics, business, social and political sphere, technologies, nature and human studies, medicine, sports, everyday language.

The elements of the array (words) are split into 4 main word classes: nouns, verbs, adjectives (including participles) and adverbs (including transgressives). The cross-lexical structure represents a matrix with cells, describing specific links between words. The most numerous collocations are "adjective - noun" and "verb – object (noun)." Synonyms and semantic derivatives have the most numerous semantic connections aiding in understanding the meanings of words. The cross-lexical array contains almost three hundred thousand words and over eight million links between them.

The analysis and development tests indicate that cross-lexical approach aid significantly when editing text as well as when studying the Russian language. Besides, invilving cross-lexical approach can substantially improve automatic text-processing.

Julia Taratukhina – Associate Professor, Department of Innovation and Business in Information Technologies, National Research University Higher School of Economics.
Address: 20, Myasnitskaya str., Moscow, 101000, Russian Federation.
E-mail: jtaratuhina@hse.ru  

Dmitry Aldunin – Student, Faculty of Business Informatics, National Research University Higher School of Economics.
Address: 20, Myasnitskaya str., Moscow, 101000, Russian Federation.
E-mail: daaldunin@edu.hse.ru

Nowadays, Internet has become the main medium of information exchange. It affects such areas as business, entertainment, advertising, e-education, mass media, various communities of interest and so on. Very often, such e-resources involve multicultural audience. There are different approaches to creation of ergonomic user interface design in different cultures. This particular paper is devoted to analysis of differentiation of such approaches and is also aimed at working out a list of recommendations on the ways to improve ergonomic user interface design of e-resources for multicultural audience.

This research will be the beginning of series of works devoted to the analysis of conformity of cultural specificity of target audience and the design features needed to ensure maximum comfort and ease of web user interface designed for this audience. The end result of the entire work will be the development of software environment that facilitates the development of interfaces, which are sensitive to appropriate culture.  

The achieved results can be of use not only for web design firms and usability analysts, but for any company involved in e-commerce whose target group can be split into several culture groups, as well as for lecturers reading a course in cross-cultural communication and ergonomic user interface design.

Maxim Hivintsev – Post-Graduate Student, 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: mkhivintsev@hse.ru

Andranik Akopov – 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: aakopov@hse.ru

One of the problems related with optimization tasks is that simultaneous optimization on a set of criteria (decision variables) requires considerable time and computing resources. Thus, as objective functions of many (more than 10) criteria generate multidimensional space of possible solutions, computing complexity of an optimizing task, as a rule, becomes a barrier to its solution within acceptable time. Analytical description and precise solution of such large-scale tasks in practice, as a rule, are not possible.

The purpose of the paper is development of the distributed evolutionary network intended for the solution of multi-criteria large-scale optimizing tasks. A new approach to developing of complex software solution regarding the integration of the distributed evolutionary network with simulation software AnyLogic is presented.

In this paper a novel approach to the solution of multi-criteria large-scale optimization tasks, in particular, in simulation systems (for example, AnyLogic) using distributed calculations is presented. The new concept of developing distributed evolutionary network is based on splitting of space of decision variables into clusters. Each computing element of the network is assigned to a particular cluster, then intermediate results are obtained within this cluster using interacting genetic algorithms. The approach is based on a distributed evolutionary network in which the effect from parallelization of computing processes is higher, than in classical “island model”. It is obtained by the ability of the algorithm to split the whole decision space on clusters, each of which is transferred to own computing process, with subsequent executing of genetic algorithm at local level. In this case the genetic algorithm operates with objective functions that have smaller number of decisions variables. The smaller size of population is required for obtaining acceptable decisions. As a result, the number of required recalculation of fitness functions decrease within the same time, and convergence of the algorithm to the final solution is significantly reduced.

A platform-independent system based on proposed approach using Java programming language is developed. It allows to integrate of the distributed evolutionary network and simulation models developed in AnyLogic.

Vladislav Podinovski – Professor, Department of Higher Mathematics, Faculty of Economics, National Research University Higher School of Economics.
Address: 20, Myasnitskaya str., Moscow, 101000, Russian Federation.
E-mail: podinovski@mail.ru

Mikhail Potapov – Leading Researcher, Institute of Automation and Design, Russian Academy of Sciences.
Address: 19/18, Brestskaya str., Moscow, 123056, Russian Federation.  
E-mail: potapov@icad.org.ru 

In most cases decision-making tasks turn to be multi-criteria: decision alternatives are evaluated in terms of a number of decision criteria. As each criterion determines its own best alternative, i.e. there are no alternative that is the best in terms of several criteria at the same time, then multi-criteria tasks are more complicated than single-criterion ones and require special methods and solutions for task performance. The best known, most popular and widely used method is a weighted sum method based on conversion of all criteria into a single generalized criterion representing a sum of criteria weighted by their relative importance coefficients (weights).

The weighted sum method (WSM) is an attractive heuristic method, however, with a number of irremediable fundamental drawbacks. The task of this article to provide a complex review of critical WSM analysis results mentioned in various scientific publications.

WSM drawbacks include but not limited to: use of non-physical values; use of constant criteria weights; no check for naturally independence of criteria; unjustified consideration of criteria rating scale as quantitative; unjustified acceptance of assumption of general criteria rating scale uniformity; unjustified non-consideration of some alternatives; unjustified consideration of importance coefficients as quantitative criteria importance evaluation; intellectual error resulting from independence of criteria normalization and weight assignment procedures; unjustified selection of functions to normalize criteria; violation of independence from irrelevant alternatives axiom.

Vladimir Nikulin – Associate Professor, Department of Mathematical Methods in Economics, Vyatka State University.
Address: 36, Moskovskaya syr., Kirov, 610000, Russian Federation.
E-mail:  vnikulin.uq@gmail.com

Svetlana Palesheva – Student, Department of Mathematical Methods in Economics, Vyatka State University.
Address: 36, Moskovskaya syr., Kirov, 610000, Russian Federation.
E-mail:  s.palesheva@gmail.com

Dar’ya Zubareva – Student, Department of Mathematical Methods in Economics, Vyatka State University.
Address: 36, Moskovskaya syr., Kirov, 610000, Russian Federation.
E-mail:  ZubarevaDasha@yandex.ru

Relevance of the problem is caused by the necessity to determine a suitable direction for the students (attendees of training courses), who are practically "living" in the Internet, by taking into account the growing number and availability of research and educational resources. The paper considers an algorithm that has been tested in real-time during the international data analysis competition VideoLectures.Net ECML / PKDD 2011 (Track 2) on the TunedIT. VideoLectures.Net platform. It is an open multimedia resource of video lectures with research and educational content. Open social and educational systems provide new opportunities for millions of students to enjoy high quality educational materials in real-time. There are a lot of lectures read by outstanding scientists and researchers within the framework of the most significant and well-known events such as conferences, summer schools, workshops and scientific activities in the various scientific fields. The main objective of the Internet resource is to promote scientific ideas and knowledge exchange, which is achieved by providing high quality educational materials not only to a scientific community, but also to a wider audience. All lectures, documents, information and links are systematized and grouped by editors, taking into account users' comments.

We suggest using two lectures (out of three of the group) in order to determine the direction of the forecast, which includes a set of predicted lectures, supplemented with appropriate frequency or empirical probability. Accordance of the forecasted set is calculated so as to predict the remaining third lecture. Further improvements have been achieved through the application of uniform ensembles based on random subsets (this approach is also known as bagging).

Oleg Ena – Director, Center of Information and Analysis Systems, Institute for Statistical Studies and Economics of Knowledge, National Research University Higher School of Economics.
Address: 20, Myasnitskaya str., Moscow, 101000, Russian Federation.
E-mail: ovena@hse.ru

Konstantin Nagaev – Senior Researcher, Center of Information and Analysis Systems, Institute for Statistical Studies and Economics of Knowledge, National Research University Higher School of Economics.
Address: 20, Myasnitskaya str., Moscow, 101000, Russian Federation.
E-mail:  knagaev@hse.ru

Roadmapping is one of the most effective instruments for long-term strategic planning and is used worldwide for decision-making: developing and implementing strategies at the corporate tier as well as competitive and functional strategies. The most complicated kind of roadmapping is technology roadmapping which helps to evaluate how the following factors influence the domain area development. Among the factors there are grand challenges, potential commercialization of key innovational technologies, desirable configuration of products and market, etc.

The research objective is to define the ways of calculating particular and integrated indicators, which concern adaptability of technology roadmap's elements and their attributes, aimed at automatization of solving such problems as foresight researching, long-term scientific forecasting and defining innovational ways of the domain area development. Thus, the paper considers solving such problems as standartizing the integrated indicators, which concern adaptability of technology roadmap's elements and their attributes, and calculating the main technology roadmapping track. Multidimensional adaptibility function is used while evaluating the technology roadmap's elements. We adapt the utility theory  MAUT to state the integrated indicator representing the aggregare function in the form of an additive convolution. To make integrated adaptability indicators more precise, we rebalance the indicators and transfer the adaptability according to the connections between the elements. Scientific and technological solutions of the paper are applied within the interactive roadmapping program complex.

Tatiana Kravchenko – Professor, Head of 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: tkravchenko@hse.ru

Requirements management is one of the main tasks of a business analyst when implementing information systems. To fulfill the objectives of a project it is highly important to manage the requirements within the members of a team and to agree with a client upon the relevance of his requirements and the monitoring system.

The paper considers modeling the requirements management while implementing informational and technological projects. Requirements management modelling has 3 steps. The first step is connected with business requirements selection which are included into the design choice and become the frame of a project. During the second step we construct a model connected with accepting, declining, specifying each requrement or qualifying it as extra work. During the third step we set the system requirements priorities and develop a work plan for a project. Developing models of decision-making requires hierarchy analyses and analytical network technique by T.L. Saaty and software for decision-making SuperDesicions.

The proposed approach was tested on the example of the function block “Deposit on the account” while implementing the “treasury” module of the SAP system. The system requirements priorities can be used while developing a work plan within the financial instrument, allocating resources and settling the completion date. On considering all the requirements received while implementing different function blocks of a project we can develop a whole work plan for the project.

Boris Slavin – Head of Center for IT expertise of The Union of CIOs of Russia, IT Company.
Address: 19, build. 6, Leninskaya Sloboda str., Moscow, 115280, Russian Federation. 
E-mail: bbslavin@gmail.com

Yamalov Ildar – Head of Agency for Information Technologies of the Republic of Bashkortostan.
Address: 45, Kirova str., Ufa, 450000, Russian Federation.  
E-mail:  yamalov.i@minsvyazrb.ru

The paper considers the results of research and modeling of information technologies (IT) industry growth in the Russian region (Republic of Bashkortostan is taken as an example) stimulated by additional investments in e-learning. Since e-learning requires the development of e-content, investments in education are becoming investments in the business that creates electronic resources. The paper provides an approach to enhance the development of the IT industry on the basis of the investment model of the region’s development.

When designing the model, the paper considers three investment sources: 1) investments in the program of citizens and employees e-education, which when implemented will encourage business and educational institutions to extend their services; 2) investments in the IT industry support funds, which are designed to attract foreign investments in the IT industry; 3) investments in the infrastructure and media projects, which provide benefits to IT companies whose developers are living in the region. The model suggests the principle of co-financing from business and government. On the whole these investments were equal, while when comparing the three sources, in certain cases either the public or the private investments were more significant.

The principle of co-financing involves more than just investment equality, it involves the measures taken by the government to encourage private investment, too. This factor leads to the fact that all investment sources are connected. Investing in the long-term e-learning program contributes to growth of IT industry. Public-and-private investment funds reduce the risk that, upon completion of support program for IT industry, its development will subside. Support for infrastructure projects, media and research activities in the field of information technologies allows the money to be kept in the region. The model shows that investments in e-learning form the infrastructure typical for SMART-region.