2014. No. 3 (29)

Maxim Amzarakov - Director, INO Uptime Technology.
Address: 9, Chasovaya str., Moscow, 125315, Russian Federation.
E-mail: m.amzarakov@uptimetechnology.ru

Rafael Sukhov - Finance Manager, INO Uptime Technology. 
Address: 9, Chasovaya str., Moscow, 125315, Russian Federation.
E-mail: r.sukhov@uptimetechnology.ru

Eugene Isaev - Professor, Head of Department for Education Stack Group, National Research University Higher School of Economics; Head of the Laboratory, P.N.Lebedev Physical Institute of the Russian Academy of Sciences.
Address: 20, Myasnitskaya str., Moscow, 101000, Russian Federation.
E-mail: eisaev@hse.ru

     Data center modularity is a new term in data processing. The given article aims at making distinction between the modular, mobile, modern and traditional data center, and reviews solutions available in the market.
     The present research provides a systematic view of the currently existing data center technology solutions, as well as the major factors influencing the cost and possible engineering pitfalls and determining basic rules of calculating and evaluating their cost and further maintenance. The concept of energy efficiency is studied here, as well as its influence on the primary cost of a data center, its maintenance cost, and thus its final cost. The conditions for the modular solutions for data processing centers emergence have also been studied here. Classifying and identifying key features allows precise positioning of the applicability of existing technologies. For this purpose, the paper provides major features of the applicability limits of available technologies, while technological solutions from different vendors are evaluated for containing engineering systems. The received estimations are presented in a convenient and comparable tabular form and.
     The research results are provided in the form of a summarizing table allowing comparing the features of each solution in several aspects: form factor, complete solution, modularity, flexibility, further development in several key engineering solutions.

Alexey Mitsyuk -Analyst, International Laboratory of Process-Aware information Systems (PAIS Lab.), National Research University Higher School of Economics. 
Address: 20, Myasnitskaya str., Moscow, 101000, Russian Federation.
E-mail: amitsyuk@hse.ru

Anna Kalenkova - Research Fellow, International Laboratory of Process-Aware information Systems (PAIS Lab.), National Research University Higher School of Economics. 
Address: 20, Myasnitskaya str., Moscow, 101000, Russian Federation.
E-mail: akalenkova@hse.ru

Sergey A. Shershakov - Research Fellow, International Laboratory of Process-Aware information Systems (PAIS Lab.), National Research University Higher School of Economics. 
Address: 20, Myasnitskaya str., Moscow, 101000, Russian Federation.
E-mail: sshershakov@hse.ru

Wil van der Aalst - Academic Supervisor, International Laboratory of Process-Aware information Systems (PAIS Lab.), National Research University Higher School of Economics; Full Professor, Department of Mathematics & Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands.
Address: P.O. Box 513, NL-5600 MB, Eindhoven, The Netherlands
E-mail: w.m.p.v.d.aalst@tue.nl

     E-trade systems are widely used to automate sales processes. Inefficiencies and bottlenecks in the sales processes lead to business losses. Conventional approaches to identifying problems require much time and result in subjective conclusions. This paper proposes an approach for the analysis of e-trade system processes based on the application of process mining techniques. Process mining aims to discover, analyze, repair and improve real business processes on the basis of behavior of an information system recorded in an event log. Using process mining techniques, we have analyzed process running in an online ticket booking information system. This work has shown that process mining can give insight into the e-trade processes and can produce information for their improvement. The case study carried out allows formulating appropriate recommendations. The article also presents the real outcome of using process mining techniques. We have generalized the applied approach and showed how it could be used to the investigation of a wide spectrum of e-trade information systems. During the case study we mostly used a software framework named ProM, which includes a substantial number of plug-ins implementing process mining methods. Using software for automatic process analysis and discovery, one should be careful with the interpretation of particular methods’ output. Pitfalls and difficulties of applying process mining techniques to the logs of e-trade systems have also been shown.

Georgy Kukharev - Full professor, Department of Multimedia Systems, West Pomeranian University of Technology
Address: 49, Żołnierska, Szczecin, 70-310, Poland
E-mail: gkukharev@wi.zut.edu.pl

Yuri Matveev - Professor of the Department of Speech Information Systems, Saint Petersburg National Research University of Information Technologies, Mechanics and Optics (ITMO University); Chief Researcher, STC Innovation Ltd.  
Address: 49, Kronverkskiy av., St. Petersburg, 197101, Russian Federation
E-mail: matveev@mail.ifmo.ru

Nadezhda Shchegoleva - Associate Professor, Department of Software Engineering and Computer Applications, Faculty of Computer Technologies and Informatics, Saint Petersburg Electrotechnical University (LETI University)
Address: 5, ul. Professora Popova, St. Petersburg, 197376, Russian Federation
E-mail: nlschegoleva@etu.ru

     The paper addresses the problem of linear barcode generation for face images. The history of the problem and possible approaches to its solution in mobile oriented systems are discussed.
     Two methods are presented: the first one is based on intensity histograms, and second one is based on intensity gradients, calculated over images using their original features. Then these features are averaged over limited number of intervals, quantized in the range of decimal digits from 0 tо 9 and converted into standard barcode. Structure of barcode generation system is proposed, and description of its blocks is presented.
     The methods have been tested by using «Face94», «Face Sketch FERET Database» databases, as well as a database composed of different age faces. The tests have demonstrated invariance of barcode in respect to changes in local sizes of face images, in tilt in the XY plane, to changes in the view, mirror rotation about vertical axis, as well as changes in facial expressions and age-related face changes.
     Therefore, the presented methods constitute novel solutions to practical applications in real conditions of dynamical parameters changes in face images. Moreover, both methods require neither big computational resources, nor application of special software packages for image processing, allowing generation of linear barcode in real time systems. A generated standard barcode contains information about a person’s face and can be used for indexing, identification, recognition, and searching for people.

Yuri Smetanin - Chief Researcher, Dorodnitsyn Computing Centre, Russian Academy of Sciences.
Address: 40, Vavilova street, Moscow, 119333, Russian Federation.
E-mail: smetanin.iury2011@yandex.ru

Mikhail Ulyanov - Professor, Department of Applied Mathematics and Systems Modeling, Institute of Communications and Media Business, Moscow State University of Printing Arts; Professor, Software Management Department, School of Software Engineering, Faculty of Computer Science, National Research University Higher School of Economics.
Address: 20, Myasnitskaya street, Moscow, 101000, Russian Federation.
E-mail: muljanov@mail.ru

     Currently various approaches to time series analysis are being investigated in terms of their forecasting. In the authors’ opinion, an approach to cluster analysis, which research object constitutes sets of time series generated by various sources, is of particular interest. The clusterization space is constructed by using generalized universal characteristics of time series each of which is a coordinate in this space. In such space for each time series there is a corresponding point in the coordinates of universal characteristics. Application of cluster analysis methods enables to identify time series that are space metric, and for the obtained clusters it is possible to solve the problem of choosing an efficient method of forecasting.
     Construction of a special metric space to analyze time series constitutes the research object of this article. The research subject is this space coordinates – generalized characteristics of time series.In their previous articles, the authors have already defined two coordinates of such space: the Kolmogorov complexity of the time series and its harmonic complexity. This paper focuses on elaboration of a new generalized characteristic of time series by using combinatorics on words technique: a measure of symbolic diversity. The application of the symbolic coding approach enables to represent time series in a space of words in a selected alphabet. Investigation of the representation generated by combinatorics on words methods enables to estimate the entropy of shifts as a function of the length of the sliding window. A measure of symbolic diversity of time series has been proposed based on investigation of specifics of the first finite difference of this function. The proposed generalized characteristic may be applied for further identification of specific features of time series; in particular as one of the axes in the clusterization space.

Leonid N. Yasnitsky - Professor, Department of Information Technologies in Business, Faculty of Business Informatics, National Research University Higher School of Economics (Perm branch); The Chairman of the Perm office of Scientific Council of the Russian Academy of Sciences on methodology of artificial intelligence
Address: 38, Studencheskaya street, Perm, 614046, Russian Federation.
E-mail: yasn@psu.ru

Dmitry V. Ivanov - Post-graduate student, Department of Information Systems and Mathematical Methods in Economics, Faculty of Economics, Perm State University  
Address: 15, Bukireva street, Perm, 614990, Russian Federation.
E-mail: idv_1988@mail.ru

Ekaterina V. Lipatova - Student, Faculty of Economics, National Research University Higher School of Economics (Perm branch);  
Address: 38, Studencheskaya street, Perm, 614046, Russian Federation.
E-mail: Lipatova_katya@mail.ru

      The object of research is the banking system of Russia. The study purpose is to build a mathematical model to estimate probability of bank bankruptcies due to license revocation.  An instrument to build the model is neural networks to be trained on financial statements of the Central Bank of the Russian Federation. The testing error of the trained and optimized neural network has constituted 6.3%.  The studies of the modeled area – the banking system of the Russian Federation – have been carried out through virtual computer experiments.  The neural network calculations have been made by changing one of fifteen bank-related input parameters with other parameters remaining constant.
      In particular, the impact of long-term liquidity ratio, the type of business legal status, the exposure to large credit risks and bank place of registration on bank bankruptcy probability has been investigated. As a result the conclusion has been formulated that the increase of long-term liquidity ratio reduces the bank bankruptcy probability. However, starting with a certain level, depending on other parameters of a specific bank, the increase of this indicator increases the probability of its bankruptcy. Essential impact on successful bank performance is exerted by bank’s business legal status, as well as the place of its registration. However, this impact is ambiguous and may manifest itself differently in each individual case, depending on many other bank parameters and its operations. A case study involving the mathematical model application to formulate recommendations to reduce bankruptcy probability of a bank is given. 

Nina L. Korovkina - Associate Professor, Department of Corporate Information Systems, Faculty of Business Informatics, National Research University Higher School of Economics. 
Address: 20, Myasnitskaya Street, Moscow, 101000, Russian Federation.
E-mail: nkorovkina@hse.ru

Maria S. Fay - Student, Hasso-Plattner-Institut für Softwaresystemtechnik, School of design thinking
Address: 2-3, Prof.-Dr.-Helmert-Str., Potsdam, D-14482, Germany.
E-mail: msfay@edu.hse.ru

      This article presents an approach to developing a methodology for IT project investment feasibility through establishing a link to company business drivers. Representing the factors enhancing the company value, business drivers enable to identify how strategic benefits from IT project implementation influence enterprise performance indicators. A business driver tree is based on the ValIT model, displacing the hierarchy of several financial factors that increase economic value added and being complemented by several industry- and company-specific nonfinancial factors, such as “internal optimization” and “ability to innovate”. Thus an underlying basis is provided to evaluate strategic feasibility of project investment.
     Such method of formulating potential benefits from information technologies (IT) in business terms is regarded as a criterion for investment decision-making and complements the Value-Based Management concept. The paper argues that in a number of cases the adequate level of company business drivers coverage by a project can compensate for the negative value of expected financial benefits. The analysis of IT investment feasibility opportunities enables to build a decision-making matrix based on risk assessment, quantitative indicators and alignment with company priorities. Implementation of the recommendations formulated in the paper is intended to ensure a higher return on IT investments (and their transparency) and to achieve harmonization of business and IT. It also enables to take into account business specifics and to carry out a more comprehensive appraisal of IT investment-related effects that mitigates the risk of inaccurate cash flow estimation. The proposed approach has been successfully tested in assessment of a potential IT projects portfolio at a large Russian manufacturing company.

Yuri P. Ekhlakov - Professor, Head of Department of Data Processing Automation, Faculty of Control Systems, Tomsk State University of Control Systems and Radioelectronics
Address: 74, Vershinina street, Tomsk, 634034, Russian Federation.
E-mail: upe@tusur.ru

Natalya V. Permyakova - Post-graduate student, Department of Data Processing Automation, Faculty of Control Systems, Tomsk State University of Control Systems and Radioelectronics
Address: 74, Vershinina street, Tomsk, 634034, Russian Federation.
E-mail: pnv@muma.tusur.ru

      This paper discusses the issue of risk assessment and analysis in development and implementation of a software promotion scheme, and demonstrates feasibility of fuzzy analysis as a mathematical tool for this purpose. It formulates the marketing goal of the scheme as being “to achieve the target sales within the specified period of time under a limited budget”. Given the clear logical connection between the goals of the scheme and the associated risks, the paper identifies three types of risks: failure to meet scheme implementation schedule, failure to meet target sales, failure to stay within the budget. Based on analysis of available publications, risk-contributing factors have been identified, a classification of such factors and their qualitative and quantitative characteristics have been offered. A real case study of building of a fuzzy risk assessment and analysis model for market launch of a Web-oriented geo-information technology for an enterprise master plan has been considered. The analysis has identified eleven input linguistic variables (risk-contributing factors) having impact on scheme risks and three output variables (degree of factors’ impact on the total risk of the project, budget overrun rate and target sales achievement rate). Two databases of rules have been built: the rules of the first database are used to determine the degree of factors’ impact on the total risk. The rules of the second database are applied to determine the risk exposure of the main goals of the scheme. The authors have used Mamdani’s fuzzy inference algorithm to calculate values of each of the risks and to offer their risk response scenarios. In practical terms the results are useful for heads of small IT companies and marketing experts in promotion of new products in industrial markets.