Summary) The abstract should follow the structure of the article (relevance, degree of exploration of the problem, the goal, the main results, conclusion) and characterize the theoretical and practical significance of the study results. The abstract should not contain wording echoing the title, cumbersome grammatical structures and abbreviations. The text should be written in scientific style. The volume of abstracts (summaries) depends on the content of the article, but should not be less than 250 words. All abbreviations must be disclosed in the summary (in spite of the fact that they will be disclosed in the main text of the article), references to the numbers of publications from reference list should not be made. The sentences of the abstract should constitute an integral text, which can be made by use of the words “consequently”, “for example”, “as a result”. Avoid the use of unnecessary introductory phrases (eg, “the author of the article considers...”, “The article presents...” and so on.)

/pdf/ministry-of-education-and-science-of-the-russian-federation-7m2jn0xnm1.pdf

Ministry of Education and Science of the Russian Federation

Mind mapping is a thinking technique that nursing leaders can use to improve their decision-making and problem-solving abilities, and to better organise their ideas.

The mind map book.

Data Ecosystems are socio-technical complex networks in which actors interact and collaborate with each other to find, archive, publish, consume, or reuse data as well as to foster innovation, create value, and support new businesses. While the Data Ecosystem field is thus arguably gaining in importance, research on this subject is still in its early stages of development. Up until now, not many academic papers related to Data Ecosystems have been published. Furthermore, to the best of our knowledge, there has been no systematic review of the literature on Data Ecosystems. In this study, we provide an overview of the current literature on Data Ecosystems by conducting a systematic mapping study. This study is intended to function as a snapshot of the research in the field and by doing so identifies the different definitions of Data Ecosystem and analyzes the evolution of Data Ecosystem research. The studies selected have been classified into categories related to the study method, contribution, research topic, and ecosystem domains. Finally, we analyze how Data Ecosystems are structured and organized, and what benefits can be expected from Data Ecosystems and what their limitations are.

Investigations into Data Ecosystems: a systematic mapping study

The way that individuals and organizations have produced and consumed data has changed with the advent of new technologies. As a consequence, data has become a tradable and valuable good. There are now Data Ecosystems, in which a number of actors interact with each other to exchange, produce and consume data. Such ecosystems provide an environment for creating, managing and sustaining data sharing initiatives. Despite Data Ecosystems are gaining importance, until now, there is no common agreement on what theories should look like in Data Ecosystems. The evidence is the lack of a well-accepted definition of the term Data Ecosystem. In order to overcome this gap, in this paper, we investigate some theoretical issues that are relevant for Data Ecosystems. Our main focus is on the aspects related to the components of a Data Ecosystem as well as to propose a common definition for a Data Ecosystem term. Therefore, the aim of our work is two-fold. First, we investigate the state of research on the Data Ecosystem field and related kinds of ecosystems, such as Business and Software Ecosystems to enable the development of a common knowledge base. Second, we extract constructs from relevant studies in order to build a common and cohesive definition for Data Ecosystems.

What is a data ecosystem

In this paper an approach for automated source-to-source translation from Cobol to Java is presented. The primary goal of this approach is to ensure that the generated code will be maintainable. In some cases this is achieved even by sacrificing complete functional equivalence of the generated code with the original program. We also intensively use encapsulation to hide away difficulties of language conversion process from the maintenance engineer. We discuss results of practical application of this approach on examples taken from the real-life programs.

Automated Cobol to Java recycling

The DocLine method designed for developing documentation for software product lines is presented. The method makes it possible to reuse document fragments with adaptation to a particular usage context. The method provides the Documentation Reuse Language (DRL) that has a graphical part (for designing the structure of documentation packages) and a text part (for implementing the documentation). It also describes a process for developing documentation and a toolset architecture based on the DSM approach and Eclipse GMF technology.

DocLine: A method for software product lines documentation development

The aim of this paper is to empirically investigate the role of knowledge management capability in the Russian high-tech sector. The high-tech complex plays an important role in the economy of the Russian Federation and accounts for 21% of the gross domestic product. The results of the previous research have shown that among the three selected antecedents the engineering processes have the most impact on knowledge management (KM) capability. However KM capability did not prove the mediating role between these antecedents and enterprise performance. Moreover, the questionnaire used was not clear enough to respondents, and the data processing tool was not appropriate either. This paper presents the next step of the study where an improved questionnaire and a modified data processing tool were used. The main findings are the following: the positive impact of chosen antecedents on KM capability has been confirmed as well as role of KM as a mediator; the peculiarities of knowledge management capability in the Russian high-tech sector have been identified.

Knowledge Management Capability Impact on Enterprise Performance in Russian High-Tech Sector

Collaborative learning is a set of various approaches in education that involve joint intellectual effort by students or students and teachers. It opposed to the traditional ’direct transmission’ model, in which learners are assumed to be passive, receptive, isolated receivers of knowledge and skills delivered by an external source. Today Computer-Supported Collaborative Learning (CSCL) is actively developed to support collaborative learning process with the help of modern information and communication technologies. Mindmaps is one of the wide-known learning technique which can be used in CSCL. In this paper we present new mindmaps Web-based tool Comapping, which provides a wide variety of ways to organize collaborative processes in education. We also describe our experiments with applying the CSLC-paradigm using Comapping while teaching Software Engineering in Saint Petersburg State University.

/pdf/computer-supported-collaborative-learning-with-mind-maps-5ffbinoal2.pdf

Computer-Supported Collaborative Learning with Mind-Maps *

The paper is devoted to the validation of visual software models. The aim of the validation is to verify whether the models match the visual project language, which is a dialect of a standard visual modeling language (UML, SDL, etc.) created with regard to specific features of the particular project on software development. In the paper, an architecture of a validator designed for both interactive and batch modes is suggested. An approach to specifying visual project languages by means of OCL constraints imposed on the UML metamodel with the subsequent automated generation of validators in accordance with the architecture described is suggested. Results of approbation of the validator and the validator generator in the framework of an industry project are presented.

OCL-Based Automated Validation Method for UML Specifications

Open data can increase transparency and accountability of a country government, leading to free

information sharing and stimulation of new innovations. This paper analyses government open data policy

as well as open data initiatives and trends in Russian Federation. The OECD analytical framework for

national open government data portals and supporting initiatives is used as the bases for the study. The key

issues of Russian open government data movement are summarized and aggregated. The paper argues the

necessity of systematic development of the open data ecosystem, the leading role of the government in data

release, a deeper business involvement, and a reduction of bureaucratic barriers.

Dmitrij V. Koznov

Papers

DocLine: A method for software product lines documentation development

Knowledge Management Capability Impact on Enterprise Performance in Russian High-Tech Sector

Computer-Supported Collaborative Learning with Mind-Maps *

OCL-Based Automated Validation Method for UML Specifications

A Survey of Open Government Data in Russian Federation