Alexander Bershadsky

Handbook of Research on Estimation and Control Techniques in E-Learning Systems

Vardan Mkrttchian (HHH University, Australia), Alexander Bershadsky (Penza State University, Russia), Alexander Bozhday (Penza State University, Russia), Mikhail Kataev (Tomsk State University of Control System and Radio Electronics, Russia & Yurga Institute of Technology (Branch) of National Research Tomsk Polytechnic University, Russia) and Sergey Kataev (Tomsk State Pedagogical University, Russia)

Research of filtration methods for reference social profile data

In recent years, information society growth was particularly noticeable. The percentage of new information on the Internet significantly increased. However, the possibility of the traditional data processing tools has been limited, so they came to replace by the Big Data technology. This concept provides new opportunities for the gathering and analysis of heterogeneous personal data from public sources. This applies to the task of building a social profile. The purpose of this work is research methods for filtering the social profile reference data from meaningless information and confidentiality of personal data. The article includes 5 logical parts. First part gives background information about existed methods and researches on this topic. Next part talks about the process of network human identification and structure of the collected data storage in the building personal social profile system. Third part touches upon the problem of reference social profile data filtering. Further its need to discuss the issue of collected personalized data confidentiality. Last part is about creation of a common solution plan to filter information with Big Data technologies. In conclusion we must say that it has achieved the goal of systematization the existing social data filtration experience for personal social profile construction system. It was also laid the necessary theoretical foundation for further deeper study of the filter data individual steps.

The Process of Personal Identification and Data Gathering Based on Big Data Technologies for Social Profiles

Currently, the problem of efficient gathering and analysis of heterogeneous data from public Internet sources is relevant to many companies working with social issues. Also it applies to the task of building a social profile for the subsequent person identification. This paper addresses the issues of direct and indirect identification a person on the Internet as well as data structure development to store the static and dynamic information of a personal social profile. The article considers the basic options of personal network identification such as an identification by profiling data, IDs, personal websites and blogs, accounts in social networks, e-mail addresses and links from other resources. After identifying it is necessary to collect and structure the detected data about a person. For this purpose, the authors proposed a basic data structure divided into static and dynamic parts. The static part is represented in the form of a relational database and contains immutable data that uniquely identify a specific human (for example, name and surname, completed educational institutions, career, curriculum vitae etc.). The dynamic part is organized as a NoSQL store and accumulates all the information about the current human activity in the network (for examples, accounts in social networks, network friends, current preferences when buying products, preferences when visiting network resources, political and social beliefs etc.).

Issues of Public Multimedia Analysis in A Socio-Cultural Researches

Analysis of public Internet data is a popular topic for socio-cultural researches. Study prospects of using personalized data led the scientists to the problem of constructing a social profile. It is structured set of information, which is able to uniquely characterize a particular person. The social profile building is carried out through the analysis of the filtered Internet open source data. Raw social profile data are subdivided on static and dynamic parts. Dynamic unstructured data includes text and multimedia information and cannot be handled by classical analytic means. The analytical task of social profile data is achieved through mathematical tools of the set theory, Big Data software and analytic tools for social media. Also, modern methods for analysis of the multimedia data are helpful. The techniques review for the analysis of multimedia content (graphics, sound) is offered. Analysis of multimedia resources is complicated by the variety of information processed types. The current work is devoted to combine existing experience by automating non-textual information processing in the task of a social profile building. Automated processing of multimedia information will improve the completeness and accuracy of the final social profile. In addition, the results of this work can be used to study the social phenomenon of viral media.

Analysis of unstructured text data for a person social profile

The greatest scientific interest for analysts are Internet open social data, because it has a direct link with all kinds of human activity. However, these data are not suitable for the application in its original form. Information should be presented in a structured, convenient, human-readable form which is called a social profile. The social profile building is carried out through the analysis of the filtered Internet open source data. Analysis of personal profile data is achieved through the use of mathematical set theory, Big Data software, NoSQL data stores and analytic tools for social media. This article discusses methods of unstructured textual data analysis in relation to a social profile. Special attention is given to the search of implicit dependences in texts using visual analysis and natural language processing means. Phase of the textual data analysis is the most important in terms of results and complicated to implement. There is the possibility to partially automate the process of information analyzing through the use of visual analysis, natural language processing (NLP), neural networks and specialized algorithms. Resulted data provide a detailed in-depth review of the social profile entities and relations. It can be used in further deeper social researches.

Techniques for Adaptive Graphics Applications Synthesis Based on Variability Modeling Technology and Graph Theory

The rapid increase in complexity of software systems in recent decades inevitably leads to a shift in focus of attention from programming techniques and approaches to principles of system engineering. Since complex software systems are quite expensive, they are expected to have a long lifetime. Systems having prolonged lifetime should be able to adapt to changes in the subject area and environment, and contribute to saving human resources as well. The problem of lifetime elongation for interactive software systems using a graphical representation of data (in particular, three-dimensional graphics) such as virtual simulators, training systems, and other similar systems is of current interest. The primary objective is to develop techniques aimed at increasing adaptability of such systems. This will provide further process development of more advanced design technologies for the software having an elongated lifetime. The main challenges of the research under discussion involve: (1) providing an overview of current techniques for adaptive software design; (2) identifying the main issues associated with the development of computer software systems having an elongated lifetime; (3) Suggesting new techniques for synthesis of adaptive software systems using graphical data representation.

Variability Modeling in the Automated System for Authoring Intelligent Adaptive Applications on the Basis of Three-Dimensional Graphics

Development of adaptive applications with an extended life cycle is one of the most promising trends in the software engineering industry. The world of modern interactive applications on the basis of three-dimensional graphics (learning applications, virtual simulators, computer games, simulation environments, etc.) is not exception. Designing interactive programs one should take into account both the variability of the user and the variability of the environment. Such a program should not be interrupted because of updating the software. The intelligent adaptive applications should be based on such models that would allow them to monitor the processes of variability and to adapt to them without having to recompile the source code. The main objectives of the work are: 1) to provide an overview of existing techniques for modeling software variability and self-adaptation; 2) to consider problems of extending the life cycle of software; 3) to offer techniques for modeling variability to design adaptive applications with support of 3D-graphics. This work was supported by the Russian Foundation for Basic Research (research project № 15-07-01553).

Concept of Complex Infrastructure Territory for Solving Problems of Integration Intersectoral Statistics

The article discusses the promising approach to solving the problems of system integration and the use of fragmented sectoral statistics within a unified intersectoral monitoring system. The approach is based on a new concept of the complex infrastructure of the territory (CIT), which is a set of anthropogenic, technogenic and natural geographic systems forming a single integrity within the selected spatial and temporal scale. It is proposed a four-level model as a formalized description of the CIT, including abstract mathematical, logical, spatial and physical levels. As part of the abstract mathematical level, it is discussed new kinds of operations on hypergraphs - dynamic restructuring hyperedges structure and installing multiple layers structure hyperedges. Moreover, the article considers the technology development universal monitoring systems by integrating arsenal of advanced information technology (OLAP, Data Mining, GIS).