Research and development workstation environment: the new class of Current Research Information Systems
Against the backdrop of the development of modern technologies in the field of scientific research, the new class of Current Research Information Systems (CRIS) and related intelligent information technologies have arisen. It was called – Research and Development Workstation Environment (RDWE) – the...
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pp_isofts_kiev_ua-article-2892024-04-28T11:37:25Z Research and development workstation environment: the new class of Current Research Information Systems Среда исследований и разработок рабочей станции: новый класс современных информационных систем исследований Дослідження та розробка середовища робочої станції: новий клас сучасних інформаційних систем наукових досліджень Palagin, O.V. Velychko, V.Yu. Malakhov, K.S. Shchurov, O.S. CRIS; RDWE; cloud-integrated environment; ontology engineering; composite web service; cloud computing; cloud learning environment UDC 004.853: 004.383 АРМ-НИ; АРМ; облачная среда; онтологический инжиниринг; композитный веб-сервис; облачные вычисления; облачная среда обучения УДК 004.853: 004.383 АРМ-НД; АРМ; хмарне середовище; онтологічний інжиніринг; композитний веб-сервіс; хмарні обчислення; хмарне середовище навчання УДК 004.853: 004.383 Against the backdrop of the development of modern technologies in the field of scientific research, the new class of Current Research Information Systems (CRIS) and related intelligent information technologies have arisen. It was called – Research and Development Workstation Environment (RDWE) – the comprehensive problem-oriented information systems for scientific research and development lifecycle support. The given paper describes design and development fundamentals of the RDWE class systems. The general information model of the RDWE class systems is developed. Also the paper represents the information model of the RDWE class system for supporting research in the field of ontology engineering – the automated building of applied ontology in an arbitrary domain area, scientific and technical creativity – the automated preparation of application documents for patenting inventions in Ukraine. It was called – Personal Research Information System. The main results of our work are focused on enhancing the effectiveness of the scientist’s research and development lifecycle in the arbitrary domain area.Problems in programming 2018; 2-3: 255-269 На фоне развития современных технологий в сфере научных исследований, возник новый класс средств компьютерных систем и соответствующих интеллектуальных информационных технологий, поддерживающих основные этапы жизненного цикла научных исследований. Этот класс систем получил название – Автоматизированное рабочее место научных исследований (АРМ-НИ) – сложные проблемно-ориентированные информационные системы поддержки полного цикла научных исследований. В работе описаны основы проектирования и разработки систем класса АРМ-НИ, разработана обобщённая информационная модель систем класса АРМ-НИ, а также описана информационная модель разработанной АРМ-НИ системы поддержки научно-технического творчества и исследований в области онтологического инжиниринга. Полученные результаты ориентированы на повышение эффективности полного научно-исследовательского цикла работы научных сотрудников.Problems in programming 2018; 2-3: 255-269 На фоні розвитку сучасних технологій в сфері наукових досліджень, виник новий клас засобів комп'ютерних систем і відповідних інтелектуальних інформаційних технологій, що підтримують основні етапи життєвого циклу наукових досліджень. Цей клас систем отримав назву – Автоматизоване робоче місце наукових досліджень (АРМ-НД) – складні проблемно-орієнтовані інформаційні системи підтримки повного циклу наукових досліджень. В роботі наведено основи проектування і розробки систем класу АРМ-НД, розроблено узагальнену інформаційну модель систем класу АРМ-НД, а також наведена інформаційна модель розробленої АРМ-НД системи підтримки науково-технічної творчості та досліджень в області онтологічного інжинірингу. Отримані результати орієнтовані на підвищення ефективності повного науково-дослідного циклу роботи наукових співробітників в довільних предметних галузях.Problems in programming 2018; 2-3: 255-269 Інститут програмних систем НАН України 2018-11-05 Article Article application/pdf https://pp.isofts.kiev.ua/index.php/ojs1/article/view/289 10.15407/pp2018.02.255 PROBLEMS IN PROGRAMMING; No 2-3 (2018); 255-269 ПРОБЛЕМЫ ПРОГРАММИРОВАНИЯ; No 2-3 (2018); 255-269 ПРОБЛЕМИ ПРОГРАМУВАННЯ; No 2-3 (2018); 255-269 1727-4907 10.15407/pp2018.02 en https://pp.isofts.kiev.ua/index.php/ojs1/article/view/289/283 Copyright (c) 2018 PROBLEMS OF PROGRAMMING |
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CRIS RDWE cloud-integrated environment ontology engineering composite web service cloud computing cloud learning environment UDC 004.853: 004.383 |
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CRIS RDWE cloud-integrated environment ontology engineering composite web service cloud computing cloud learning environment UDC 004.853: 004.383 Palagin, O.V. Velychko, V.Yu. Malakhov, K.S. Shchurov, O.S. Research and development workstation environment: the new class of Current Research Information Systems |
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CRIS RDWE cloud-integrated environment ontology engineering composite web service cloud computing cloud learning environment UDC 004.853: 004.383 АРМ-НИ АРМ облачная среда онтологический инжиниринг композитный веб-сервис облачные вычисления облачная среда обучения УДК 004.853: 004.383 АРМ-НД АРМ хмарне середовище онтологічний інжиніринг композитний веб-сервіс хмарні обчислення хмарне середовище навчання УДК 004.853: 004.383 |
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Article |
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Palagin, O.V. Velychko, V.Yu. Malakhov, K.S. Shchurov, O.S. |
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Palagin, O.V. Velychko, V.Yu. Malakhov, K.S. Shchurov, O.S. |
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Palagin, O.V. |
| title |
Research and development workstation environment: the new class of Current Research Information Systems |
| title_short |
Research and development workstation environment: the new class of Current Research Information Systems |
| title_full |
Research and development workstation environment: the new class of Current Research Information Systems |
| title_fullStr |
Research and development workstation environment: the new class of Current Research Information Systems |
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Research and development workstation environment: the new class of Current Research Information Systems |
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research and development workstation environment: the new class of current research information systems |
| title_alt |
Среда исследований и разработок рабочей станции: новый класс современных информационных систем исследований Дослідження та розробка середовища робочої станції: новий клас сучасних інформаційних систем наукових досліджень |
| description |
Against the backdrop of the development of modern technologies in the field of scientific research, the new class of Current Research Information Systems (CRIS) and related intelligent information technologies have arisen. It was called – Research and Development Workstation Environment (RDWE) – the comprehensive problem-oriented information systems for scientific research and development lifecycle support. The given paper describes design and development fundamentals of the RDWE class systems. The general information model of the RDWE class systems is developed. Also the paper represents the information model of the RDWE class system for supporting research in the field of ontology engineering – the automated building of applied ontology in an arbitrary domain area, scientific and technical creativity – the automated preparation of application documents for patenting inventions in Ukraine. It was called – Personal Research Information System. The main results of our work are focused on enhancing the effectiveness of the scientist’s research and development lifecycle in the arbitrary domain area.Problems in programming 2018; 2-3: 255-269 |
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Інститут програмних систем НАН України |
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2018 |
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https://pp.isofts.kiev.ua/index.php/ojs1/article/view/289 |
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Інформаційні системи
© O.V. Palagin, V.Yu. Velychko, K.S. Malakhov, O.S. Shchurov, 2018
ISSN 1727-4907. Проблеми програмування. 2018. № 2–3. Спеціальний випуск 255
UDC 004.853: 004.383
RESEARCH AND DEVELOPMENT WORKSTATION
ENVIRONMENT: THE NEW CLASS OF CURRENT RESEARCH
INFORMATION SYSTEMS
O.V. Palagin, V.Yu. Velychko, K.S. Malakhov, O.S. Shchurov
Against the backdrop of the development of modern technologies in the field of scientific research, the new class of Current Research
Information Systems (CRIS) and related intelligent information technologies have arisen. It was called – Research and Development
Workstation Environment (RDWE) – the comprehensive problem-oriented information systems for scientific research and development
lifecycle support. The given paper describes design and development fundamentals of the RDWE class systems. The general information
model of the RDWE class systems is developed. Also the paper represents the information model of the RDWE class system for supporting
research in the field of ontology engineering – the automated building of applied ontology in an arbitrary domain area, scientific and
technical creativity – the automated preparation of application documents for patenting inventions in Ukraine. It was called – Personal
Research Information System. The main results of our work are focused on enhancing the effectiveness of the scientist’s research and
development lifecycle in the arbitrary domain area.
Key words: CRIS; RDWE; cloud-integrated environment; ontology engineering; composite web service; cloud computing; cloud learning
environment.
На фоні розвитку сучасних технологій в сфері наукових досліджень, виник новий клас засобів комп'ютерних систем і відповідних
інтелектуальних інформаційних технологій, що підтримують основні етапи життєвого циклу наукових досліджень. Цей клас
систем отримав назву – Автоматизоване робоче місце наукових досліджень (АРМ-НД) – складні проблемно-орієнтовані
інформаційні системи підтримки повного циклу наукових досліджень. В роботі наведено основи проектування і розробки систем
класу АРМ-НД, розроблено узагальнену інформаційну модель систем класу АРМ-НД, а також наведена інформаційна модель
розробленої АРМ-НД системи підтримки науково-технічної творчості та досліджень в області онтологічного інжинірингу.
Отримані результати орієнтовані на підвищення ефективності повного науково-дослідного циклу роботи наукових співробітників в
довільних предметних галузях.
Ключові слова: АРМ-НД; АРМ; хмарне середовище; онтологічний інжиніринг; композитний веб-сервіс; хмарні обчислення; хмарне
середовище навчання.
На фоне развития современных технологий в сфере научных исследований, возник новый класс средств компьютерных систем и
соответствующих интеллектуальных информационных технологий, поддерживающих основные этапы жизненного цикла научных
исследований. Этот класс систем получил название – Автоматизированное рабочее место научных исследований (АРМ-НИ) –
сложные проблемно-ориентированные информационные системы поддержки полного цикла научных исследований. В работе
описаны основы проектирования и разработки систем класса АРМ-НИ, разработана обобщённая информационная модель систем
класса АРМ-НИ, а также описана информационная модель разработанной АРМ-НИ системы поддержки научно-технического
творчества и исследований в области онтологического инжиниринга. Полученные результаты ориентированы на повышение
эффективности полного научно-исследовательского цикла работы научных сотрудников.
Ключевые слова: АРМ-НИ; АРМ; облачная среда; онтологический инжиниринг; композитный веб-сервис; облачные вычисления;
облачная среда обучения.
Introduction
The development of modern technologies increasingly covers the field of intellectual activity and, especially, in
the field of scientific research and development. The new class of Current Research Information Systems and related
intelligent information technologies have arisen that support the main stages of the scientific research and development
lifecycle, starting with the semantic analysis of the information & data material of arbitrary domain area and ending
with the formation of constructive features of innovative proposals. It was called – Research and Development
Workstation Environment (RDWE) – the comprehensive problem-oriented information systems for scientific research
and development support. A distinctive feature of such systems and technologies is the possibility of their problematic
orientation to various types of scientific research and development by combining on a variety of functional services and
adding new ones within the cloud-integrated environment (inside the Ubuntu open source operating system as the
integrating cloud environment for instance).
Current research information systems
In the modern English-speaking scientific environment, a steady term – Current Research Information System
(CRIS) [1] was introduced to designate scientific information systems for access to scientific and academic information.
It is important to emphasize that the definition of CRIS also specifies that CRIS is not only intended for direct access to
information sources of science but also, according to the ERGO project [2], for:
to facilitate access to national scientific and technical information services;
to identify the main existing information sources and to evaluate access possibilities and the potential for the
utilization of these sources at European level;
to invite national data hosts to offer their research and development (R&D) information and to make this
information searchable for the user.
Інформаційні системи
256
The EuroCRIS [3] organization was founded in 2002, is an international not-for-profit association, that brings
together experts on research information in general and research information systems. The mission of EuroCRIS is to
promote cooperation within and share knowledge among the research information community and interoperability of
research information through CERIF [4] – the Common European Research Information Format. Areas of interest also
cover research databases, CRIS related data like scientific datasets, (open access) institutional repositories, as well as
data access and exchange mechanisms, standards and guidelines and best practice for CRIS. The EuroCRIS provides
the framework for the flow of information/data between a broad variety of stakeholders: researchers, research managers
and administrators, research councils, research funders, entrepreneurs, and technology transfer organizations.
The basic user categories of modern CRIS and their information demands are listed in [5] and represented in
table 1.
Table 1. The basic categories of CRIS and their information demands
Users group Information demands
Researchers
Search for scientific results, collaborators, equipment and material resources,
projects financing. Easy access to relevant information and associated
software, processor power, storage systems and detectors to collect more data
to overcome incomplete or inconsistent information – the paradigm of
Service-Oriented Science (SOS) in which web services are virtual access
points to data, computational resources, R&D environments [6].
Teachers, students
Implementation of the recent research results in the educational process and
mastering of research results in the learning process. The organization of
service support of e-learning at the university, especially at the replaced
university [7]. Applying to the educational process the modern paradigm of
Service-Oriented Learning (SOL) implemented as Personal Learning
Environments, Virtual Learning Environments, Learning Management
Systems, Education-as-a-Service models, and Cloud Learning Environments –
using RDWE systems as an E-learning/education-oriented environment.
Institute’s board of administrators,
analysts
Research management and administration, statistical reporting on recent
achievements, determining the role of research institutes and researchers in
the research process.
Experts, financiers, organizations
that provide financial resources
Project reports, estimation of expected and obtained research results.
Research project chairman’s
Research processes coordination, search for collaborators and search for
research funders for project financing.
Investors, research funders,
industry
Searching for technology, engineering, and hunting experts who can evaluate
the technology.
Community and media
Acquisition information and data in the accessible form for perception and
understanding.
This user’s groups and their information demands determine the main types of information/data sources that
CRIS works with: progress reports, project results reports, personal information and data, scientific publication,
organizations, projects, research results, technology and engineering, patents, fund programs, expert assessments,
digital libraries, encyclopedias and dictionaries, websites, mailing lists, social networks, databases, computing
resources, technological normative and other documents, educational and museum resources. Also, this user’s groups
and their information demands determine the main types of services provided by CRIS: reuse of scientific
developments, methodologies, and technologies; information search; targeted dissemination of information; messages
services; bridging of horizontal and vertical relations between organizations; backup data storage and archival
information/data repository; support for the educational process; providing analytical services.
Requirements for CRIS in the information management aspect of strategic research management has been
analyzed in [8], which describes the types of research managerial activities, introduces the currently available
information sources and how the information found in these is applied today.
The paper [9] describes requirements for CRIS to effective dissemination of technologies, the management of
scientific programs and functioning of funds for research funding. Noted the importance of CRIS systems to collaborate
Інформаційні системи
257
researchers and to support the information functioning of funds. Also represented the basic lifecycle of scientific
programs and the information demands of participants at each phase of the cycle.
The CORDIS [10] portal emphasized the basic use cases of scientific portals for researchers:
keep up-to-date on current research findings and strategic directions;
identify funding sources for R&D;
find partners to cooperate in R&D activities and share expertise;
form transnational consortia for exploitation of research results;
promote and locate transferable technologies, and more.
In the field of scientific communication and collaboration next tools are used: telecommunications services of
direct interaction (video chat, video conference, voice calls, text and video messages) – Skype, Viber, VSee etc.; cloud-
based sets of team collaboration tools and services – Slack etc.; web conference platforms for various types of online
collaborative services including webinars, webcasts, and peer-level web meetings – Adobe Connect etc.; services of
indirect communication – email; social services on the Internet – social networks with instant messaging such as
Facebook and Telegram. These services are widespread with off-the-shelf software and in the availability of
communication bus with sufficient bandwidth allow instant scientific communication.
CRIS can include systems that combine social network technologies as well as usual file repository technologies.
As commercial projects, such CRIS is oriented to one of the following business models:
- to sell user’s and subscriber’s data to advertisers that provide targeted ads, which usually costs more than
usual. Also, the platform offers universities and corporations the option to post job listings for free, but generates
revenue by charging some money for increased visibility (ResearchGate) [11, 12];
- to carry out analytics for the uploaded content and recommend suitable research activity for a fee. Also, the
platform generates revenue by providing trending research data to R&D institutions that can improve the quality of their
decisions (Academia.edu) [12, 13];
- to provide additional paid services for storage of data (publications, raw data, and media files) – the
individual researcher is served with a freemium model, users can upgrade their cloud space for a monthly fee; to
organize online discussion community platforms, forums for sharing and discussing research. Also, the platform offers
real-time analytics to libraries for a fee and has opened its application programming interface (API) for programmers
who build third-party apps on top of the data generated across the platform (Mendeley) [12, 14].
According to demands of the scientific community members Academia.edu and ResearchGate provide the
following basic features for registered users:
- members may “follow” a research interest, in addition to following other individual members;
- allows its users to create a profile, upload their work(s), select areas of interests;
- platform indexes self-published information on user profiles to suggest members connect with others who
have similar interests.
- private chat rooms where users can share data, edit shared documents, or discuss confidential topics;
- a research-focused job board feature;
- ResearchGate publishes a citation impact measurement in the form of an “RG Score”;
- Academia.edu proclaims it supports the open science or open access movements and instant distribution of
research, and a peer-review system that occurs alongside distribution, instead of prior to it.
However, the Academia.edu service is not designed to provide comprehensive information support for scientific
research, and users, in general, posts published articles to the system. Unlike Academia.edu, ResearchGate has more
diverse features. People that wish to use the site need to have an email address at a recognized institution or to be
manually confirmed as a published researcher to sign up for an account. Members of the site each have a user profile
and can upload research output including papers, data, chapters, negative results, patents, research proposals, methods,
presentations, and software source code. Users may also follow the activities of other users and engage in discussions
with them. Users are also able to block interactions with other users. ResearchGate has a blogging feature for users to
write short reviews on peer-reviewed articles. When a member posts a question, it is fielded to others that have
identified on their user profile that they have a relevant expertise.
The social networking Internet services Academia.edu and ResearchGate are largely aimed at forming the so-
called “name” of the scientist and serving a research-focused job board feature.
General design principles (approaches) of the CRIS-like systems for scientific R&D support
There are different approaches to define the structure of research activities and the research lifecycle [15, 16].
The review of these approaches, as well as the analysis of research activities, allows defining its basic types, which
regulate its structure and are invariant in relation to the domain area, content, methods, and approaches of the research
activity. The basic types of research activities are [16]:
Інформаційні системи
258
- Descriptive vs. Analytical: Descriptive research includes surveys and fact-finding enquiries of different
kinds. The major purpose of descriptive research is the description of the state of affairs as it exists at present. In social
science and business research, we quite often use the term Ex post facto research for descriptive research studies. The
main characteristic of this method is that the researcher has no control over the variables; he can only report what has
happened or what is happening. Most ex-post facto research projects are used for descriptive studies in which the
researcher seeks to measure such items as, for example, the frequency of shopping, preferences of people, or similar
data. Ex post facto studies also include attempts by researchers to discover causes even when they cannot control the
variables. The methods of research utilized in descriptive research are survey methods of all kinds, including
comparative and correlational methods. In analytical research, on the other hand, the researcher must use facts or
information already available and analyze these to make a critical evaluation of the material;
- Applied vs. Fundamental: Research can either be applied (or action) research or fundamental (to basic or
pure) research. Applied research aims at finding a solution for an immediate problem facing a society or an
industrial/business organization, whereas fundamental research is mainly concerned with generalizations and with the
formulation of a theory. Research concerning some natural phenomenon or relating to pure mathematics are examples
of fundamental research. Similarly, research studies, concerning human behavior carried on with a view to generalize
about human behavior, are also examples of fundamental research, but research aimed at certain conclusions (say, a
solution) facing a concrete social or business problem is an example of applied research. Research to identify social,
economic, or political trends that may affect an institution or the copy research (research to find out whether certain
communications will be read and understood) or the marketing research or evaluation research are examples of applied
research. Thus, the central aim of the applied research is to discover a solution for some pressing practical problem,
whereas basic research is directed towards finding information that has a broad base of applications and thus, adds to
the already existing organized body of scientific knowledge;
- Quantitative vs. Qualitative: Quantitative research is based on the measurement of quantity or amount. It is
applicable to phenomena that can be expressed in terms of quantity. Qualitative research, on the other hand, is
concerned with the qualitative phenomenon, i.e., phenomena relating to or involving quality or kind. For instance, when
we are interested in investigating the reasons for human behavior (i.e., why people think or do certain things), we quite
often talk of ‘Motivation Research’, an important type of qualitative research. This type of research aims at discovering
the underlying motives and desires, using in-depth interviews for the purpose. Other techniques of such research are
word association tests, sentence completion tests, story completion tests and similar other projective techniques.
Attitude or opinion research i.e., research designed to find out how people feel or what they think about a subject or
institution is also qualitative research. Qualitative research is especially important in the behavioral sciences where the
aim is to discover the underlying motives of human behavior. Through such research, we can analyze the various
factors which motivate people to behave in a manner or which make people like or dislike a thing. It may be stated,
however, that to apply qualitative research in practice is relatively a difficult job and therefore, while doing such
research, one should seek guidance from experimental psychologists;
- Conceptual vs. Empirical: Conceptual research is that related to some abstract idea(s) or theory. It is
generally used by philosophers and thinkers to develop new concepts or to reinterpret existing ones. On the other hand,
empirical research relies on experience or observation alone, often without due regard for system and theory. It is data-
based research, coming up with conclusions which are capable of being verified by observation or experiment. We can
also call it as the experimental type of research. In such a research, it is necessary to get at facts firsthand, at their
source, and actively to go about doing certain things to stimulate the production of desired information. In such a
research, the researcher must first provide himself with a working hypothesis or guess as to the probable results. He
then works to get enough facts (data) to prove or disprove his hypothesis. He then sets up experimental designs which
he thinks will manipulate the persons or the materials concerned so as to bring forth the desired information. Such
research is thus characterized by the experimenter’s control over the variables under study and his deliberate
manipulation of one of them to study its effects. Empirical research is appropriate when the proof is sought that certain
variables affect other variables in some way. Evidence gathered through experiments or empirical studies is today
considered to be the most powerful support possible for a given hypothesis;
- Some Other Types of Research: All other types of research are variations of one or more of the above-stated
approaches, based on either the purpose of research, or the time required to accomplish research, on the environment in
which research is done, or based on some other similar factor. Form the point of view of time, we can think of research
either as one-time research or longitudinal research. In the former case, the research is confined to a single time-period,
whereas in the latter case the research is carried on over several time-periods. Research can be field-setting research or
laboratory research or simulation research, depending upon the environment in which it is to be carried out. Research
can as well be understood as clinical or diagnostic research. Such research follows case-study methods or in-depth
approaches to reach the basic causal relations. Such studies usually go deep into the causes of things or events that
interest us, using very small samples and very deep probing data gathering devices. The research may be exploratory or
it may be formalized. The objective of exploratory research is the development of hypotheses rather than their testing,
whereas formalized research studies are those with substantial structure and with specific hypotheses to be tested.
Historical research is that which utilizes historical sources like documents, remains, etc. to study events or ideas of the
past, including the philosophy of persons and groups at any remote point of time. Research can also be classified as
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conclusion-oriented and decision-oriented. While doing conclusion-oriented research, a researcher is free to pick up a
problem, redesign the enquiry as he proceeds and is prepared to conceptualize as he wishes. Decision-oriented research
is always for the need of a decision maker and the researcher, in this case, is not free to embark upon research according
to his own inclination. Operations research is an example of decision-oriented research since it is a scientific method of
providing executive departments with a quantitative basis for decisions regarding operations under their control.
The general principle that should be considered when designing CRIS-like systems for scientific research and
technical research creativity support is that the system must fully cover all the basic types of research activities,
implement the full research lifecycle. Based on this basic principle can identify the main entities of CRIS-like
systems [17]:
1. User with all related attributes.
2. Organizations which are related to users.
3. Organizations and foundations (research funders etc.) that provide grant support for scientific research.
4. Scientific communities with the ability to carry on a discussion between united users (analogues of chats
and forums).
5. Scientific journals and publishers.
6. Bibliographies that can be created both by the users themselves and by the communities.
7. Publications of users that can be linked to publishers and periodicals.
8. Projects that can integrate users, bibliographies, documents, and general data, and can also be related to
grants of research funders and organizations.
It should be noted that all these and other entities that may appear in the future should be able to communicate
with each other. This communication will allow the flexible and comprehensive solution of the tasks of organizing of
co-operative research activities by communities of scientists, to receive various reports, for example, lists of
publication, lists of publisher’s journals, statistical reports on the research activity of employees, etc. The integrated
system should be a universal instrument or utility toolkit not only for the purposes of supporting scientific research
but also for the analysis of the activities of scientific organizations, foundations, and publishers.
Another important principle for developing the CRIS-like system is the choice of a user identification model.
There is no need to create multiple user accounts in different network services. For identification, it is suitable to
choose one of the open identifying system: Open Researcher and Contributor ID (ORCID) [18], ResearcherID [19]
or OpenID [20]. For scientists, it is suitable to choose ORCID – is a nonproprietary alphanumeric code to uniquely
identify scientific and other academic authors and contributors. It is implemented in the open project ORCID. The
ORCID organization offers an open and independent registry intended to be the de facto standard for contributor
identification in research and academic publishing. The ORCID community includes everyone in the research
community who recognizes the need for researchers to be uniquely identified and connected with their contributions
and affiliations. This means individual researchers, organizations involved in research – universities, laboratories,
commercial research companies, research funders, publishers, patent offices, data repositories, professional societies,
and more – as well as organizations that build systems that support information management among and between
these groups. Third-party tools allow the migration of content from other services into ORCID, for example,
Mendeley2ORCID, for Mendeley.
General requirements to the CRIS-like systems for scientific R&D support
In [4, 17, 21] reviewed general requirements and specifications for the CRIS-like systems. Summing up all these
papers and resources, we can point out the most general requirements and specifications for the CRIS-like systems.
1. Data coverage. Coverage of the maximum amount of information resources and repositories. It is
necessary to create data input procedures and data entry points to collect information. The following options for
inputting data are available:
interactive user input;
data collection over the Internet uses the specialized software – web crawlers, Internet bots, intelligent
agents etc.;
data exchange and CRIS integration with other CRIS-like systems.
2. Data relevance. The automatic collection of information over the Internet may accumulate out of date data.
This problem can be solved by:
to create detailed formats for submitting metadata about resources, directories for thematic
classification of resources, as well as adding metadata to resources. It is problematic to require authors clearly and
accurately comply with the metadata formats and carefully add metadata to text sources that require additional work
and explore metadata formats;
to classify and categorize all the information resources collected by experts (users) and web crawlers.
Also, to indicate the degree of probability of belonging to the search query depending on its source;
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the exact indication of the search area to the search engines, as well as the criteria for the quality of the
collected information;
to create the ontologies of resources according to the user demands. The classification of resources by
experts in accordance with these ontologies.
3. Completeness and validity of data. The data completeness problem is solved by the same method as the
coverage data problem solved. The validity of data problem can be solved by:
for interactive input – the restriction of input only by authenticated users;
for the automated search engines – the restriction activity area of the web crawler to collect information;
for input data exchange with other systems – by establishing precise filters for imported information
resources;
for all systems – verification and classification of the input data.
4. The maintenance of the intelligent user query service. User query services should support search by
attributes, resource overviews by category, and full-text search using semantic document analysis.
5. Support various levels of abstraction for data representation. Internet efficiency for CRIS-like systems is
not a direct consequence of the amount of available information/data or even its quality but is a direct consequence of
the rate and accuracy of the selection of information for the demands of researchers. Support various levels of
abstraction for information/data representation allows precipitating the information/data search without its loss. CRIS-
like systems need to support several levels of abstraction, and not just full-text descriptions. It is necessary to represent
information in a compact form (document ontology, abstract, annotation, compendium, or recap) selectively – from
brief descriptions for the maximum quick search to very detailed descriptions of information objects.
6. The historicity of the scientific information. The specificity of the scientific information is quite a short
period of its relevance. For many types of the scientific information resources, it is important to keep the description of
the lifecycle of these resources and be able to restore the status of the resource at any given time.
7. Maintaining an archival repository of scientific information. As it was noted above, the most part of the
scientific information fast becomes outdated. But there are information resources that may be relevant for a long time.
These include: the long-term documents, the patents, and the multimedia information about historical events. Scientific
reports of institutes, speeches of scientists and researchers can also have a huge historical value, becoming later even
more valuable. Therefore, the system should support the possibility of long-term storage of the information resources.
8. Distributed architectures support for the information systems. This requirement is a prerequisite for
completeness, authenticity, and relevance of information. The experience of usage of the CRIS-like systems has shown
that it is difficult to implement, and in many cases, even impossible, the creation of centralized scientific systems that
covers scientific information in some domain area of science, or in some country. In [22] Hale University researchers
have proven proposals for the creation of administrative and technical mechanisms for the functioning of virtual
distributed scientific libraries. In terms of the distributed computing environment, CRIS-like systems meet the
following requirements:
information exchange protocols support with other information systems;
accepted metadata standards support for the export/import of data and the usage of metadata to describe
and to specify resources;
information verification feature;
linking to internal resources support for both: in user interfaces and at the system level;
online service for accessing links to other websites and documents;
selection of resources in the intellectual process, according to published quantitative and thematic
criteria;
availability of descriptions of the resource’s content in the range from brief annotation to detailed
review;
intelligently created structure or scheme for navigation in the resource space. A good example of such
structure can be an ontological scheme of resources.
For some domain areas of research, it is also important that the scientific information system provides the ability
to use computational resources that are based on generally accepted standards.
The basic concepts of the workstation environment (WE) consider to [23, 24] are – is an individual complex of
information resources, software, hardware, organizational and technological facilities (tools) for individual or collective
usage, united to perform certain functions of the employee. WE provide the worker with all the tools and facilities
necessary to perform certain functions.
to solve a certain class of tasks and problems, united by a common information processing technology;
to formalize professional knowledge – the possibility of providing with the help of WE the automation
of new functions and solving new tasks in the process of accumulation of experience with the system;
to automate data processing in a specific area of activity;
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to provide the ability of data processing by the user itself;
to provide user access to the set of software; hardware and information tools & facilities;
to create custom comfortable working conditions and friendly user interface to communicate & interact
with the system;
to combine the system with other elements of information processing and modification, expansion
capabilities of WE without interrupting its operation.
The structure and composition of the elements of any WE depend on its purpose, the composition of tasks,
structure, software, the method of fixing data in primary documents and more. The WE structure is composed of:
- the functional part of WE;
- the software part of WE;
- the hardware part of WE;
- the information support part of WE. This is a set of facilities and methods for building an information
base, which is divided into a machine and internal machine.
General principles of the Research and Development Workstation Environment design
Scientific and research communities create networks that combine digital libraries, file repositories, archival
information/data repositories, web servers with scientifically relevant information. The key objective factor that must
be taken into account when designing, developing and using the workstation for the Ukrainian researcher is the
limited financial resources appropriated for the development and maintenance of the workplace software, which
results in the following consequences: the inability to involve the necessary number of specialists to the development
process; minimizing the costs of the software implementing, maintaining and updating; system development for
several years by the group of developers whose composition (body) is changing; the necessity to maximize the reuse
of software that has already been developed. Next we define the general development principles of the RDWE and
its components.
1. Modular design. The development of any information system, especially that aimed at solving the
complex variety of problems and tasks, should be based on a modular design principle [17]. Modular design, or
“modularity in design”, is a design approach that subdivides a system into smaller parts called modules or skids, that
can be independently created and then used in different systems. A modular system can be characterized by
functional partitioning into discrete scalable, reusable modules; rigorous use of well -defined modular interfaces; and
making use of industry standards for interfaces. When creating a modular system, instead of creating a monolithic
application (where the smallest component is the whole), several smaller modules are written separately so that,
when composed together, they construct the executable application program. Typically, these are also compiled
separately, via separate compilation, and then linked by a linker. A just-in-time compiler may perform some of this
construction “on-the-fly” at runtime. This makes modular designed systems if built correctly, far more reusable than
a traditional monolithic design, since all (or many) of these modules may then be reused (without change) in other
projects.
2. Microservices-based architecture (MSA). Microservices is a variant of the service-oriented architecture
architectural style that structures an application as a collection of loosely coupled services [25]. In MSA, services
should be fine-grained and the protocols should be lightweight. The benefit of decomposing an application into
different smaller services is that it improves modularity and makes the application easier to understand, develop and
test. There is no industry consensus yet regarding the properties of microservices, and an official definition is
missing as well. Some of the defining characteristics that are frequently cited include [26]: services in MSA are often
processes that communicate with each other over a network in order to fulfill a goal using technology-agnostic
protocols such as HTTP; services in MSA should be independently deployable; services are easy to replace; services
are organized around capabilities, e.g., user interface front-end, recommendation, logistics, billing, etc.; services can
be implemented using different programming languages, databases, hardware and software environme nt, depending
on what fits best; services are small in size, messaging enabled, bounded by contexts, autonomously developed,
independently deployable, decentralized and built and released with automated processes.
3. Сross-platform software (also multi-platform software or platform-independent software). The
researcher must have access to the services provided by the RDWE from various types of devices operating under
the control of various operating systems. One of the ways to achieve this goal can be access to services through an
adaptive user interface of the web applications. Web applications are typically described as cross-platform because,
ideally, they are accessible from any of various web browsers within different operating systems. Such applications
generally employ a client-server system architecture and vary widely in complexity and functionality. This wide
variability significantly complicates the goal of cross-platform capability, which is routinely at odds with the goal of
advanced functionality. Basic web applications perform all or most processing from a stateless server and pass the
result to the client web browser. All user interaction with the application consists of simple exchanges of data
requests and server responses. Implementation mechanisms:
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Node.js [27] server platform – is an open-source, cross-platform JavaScript run-time environment for
executing JavaScript code server-side. Node.js enables JavaScript to be used for server-side scripting and runs scripts
server-side to produce dynamic web page content before the page is sent to the user's web browser. Consequently,
Node.js has become one of the foundational elements of the “JavaScript everywhere” paradigm, allowing web
application development to unify around a single programming language, rather than rely on a differ ent language for
writing server-side scripts. Node.js brings event-driven programming to web servers, enabling development of fast
web servers in JavaScript. Developers can create highly scalable servers without using threading, by using a
simplified model of event-driven programming that uses callbacks to signal the completion of a task. Node.js
connects the ease of a scripting language (JavaScript) with the power of Unix network programming. Node.js
applications can run on most modern operating systems (Linux, macOS, Microsoft Windows and Unix servers) and
hardware platforms (x86, x64, ARMv6, ARMv7, ARMv8).
The adaptive user interface of the web application with responsive web design using Pug (is a
template engine for Express), Bootstrap (is a design and style framework), JQuery library and AJAX (an approach to
building user interfaces for web applications) – is a web design approach aimed at crafting the visual layout of sites
to provide an optimal viewing experience – easy reading and navigation with a minimum of resizing, panning, and
scrolling – across a wide range of devices, from mobile phones to desktop computer monitors.
Storage service powered by MongoDB – is a free and open-source cross-platform document-oriented
database program. Classified as a NoSQL database program, MongoDB uses JSON-like documents with schemas.
4. JSON/XML data model and REST architectural style (RESTful web services). JavaScript Object Notation
or JSON – is an open-standard file format that uses human-readable text to transmit data objects consisting of
attribute-value pairs and array data types (or any other serializable value). It is a very common data format used for
asynchronous browser-server communication. JSON standard defines a metalanguage, based on which, by imposing
restrictions on the structure and content of documents, specific, domain-oriented markup languages are determined.
The author of the document creates its structure, builds the necessary links between the elements, and uses those
commands that meet its requirements and asks for the type of markup that it needs to handle the documents. Creating
the correct structure of the information exchange mechanism at the beginning of the system project development,
many future problems can be avoided due to the incompatibility of the data formats that are used by the various
components of the system. Representational state transfer (REST) [28] or RESTful web services are a way of
providing interoperability between computer systems on the Internet. REST-compliant Web services allow
requesting systems to access and manipulate textual representations of Web resources using a uniform and
predefined set of stateless operations. In a RESTful Web service, requests made to a resource's URI will elicit a
response that may be in XML, HTML, JSON or some other defined format. The response may confirm that some
alteration has been made to the stored resource, and it may provide hypertext links to other related resources or
collections of resources. Using HTTP, as is most common, the kind of operations available include those predefined
by the HTTP methods GET, POST, PUT, DELETE and so on. The RESTful architectural style possesses the
following constraints [29]:
Client/Server: Separation of concerns, exemplified by a client–server architecture. The idea is that
different components can evolve independently—the user interface in the client can evolve separately from the
server, and the server is simpler.
Stateless: The client–server interaction is stateless. There is no stored context on the server. Any
session information must be kept by the client.
Cacheable: Data in a response (a response to a previous request) is labeled as cacheable or non-
cacheable. If it is cacheable, the client (or an intermediary) may reuse that for the same k ind of request in the future.
Uniform Interface: There is a uniform interface between components. In practice, there are four
interface constraints: resource identification – requests identify the resources they are operating on (by a URI, for
example); resource manipulation through the representation of the resource – when a client or server that has access
to a resource, it has enough information based on understanding the representation of the resource to be able to
modify that resource; messages are self-descriptive – the message contains enough information to allow a client or
server to handle the message, this is normally done through the use of Internet Media types (MIME types); use of
hypermedia to change the state of the application – for example, the server provides hyperlinks that the client uses to
make state transitions.
Layered System: Components are organized in hierarchical layers; the components are only aware of
the layer within which the interaction is occurring. Thus, a client connecting to a server is not aware of any
intermediate connections. Intermediate filter components can change the message while it is in transit: because the
message is self-descriptive and the semantics are visible, the filter components understand enough about the message
to modify it.
Code on Demand: Code on demand is optionally supported, that is, clients can download scripts that
extend their functionality.
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5. Free/Libre and open-source software (FLOSS). FLOSS is software that can be classified as both free
software and open-source software. That is, anyone is freely licensed to use, copy, study, and change the software in
any way, and the source code is openly shared so that people are encouraged to voluntarily improve the design of the
software. The benefits of using FLOSS can include decreased software costs, increased security, and stability
(especially regarding malware), protecting privacy, and giving users more control over their own hardware. The use
of free software will significantly save money at the stages of development and implementation of the RDWE.
6. Cloud computing. Cloud computing [30] can provide interaction between different CRIS-like systems
(such as RDWE) over the Internet, with the optimal distribution of load between local and remote servers. In cloud
computing paradigm, computer resources and capacity available to the user as Internet services for data processing.
Cloud-computing providers offer their “Services” according to different models, of which the three standard models
per NIST (The National Institute of Standards and Technology) [31] are Infrastructure as a Service (IaaS), Platform
as a Service (PaaS), and Software as a Service (SaaS). These models offer increasing abstraction; they are thus often
portrayed as a layer in a stack: infrastructure-, platform- and software-as-a-service, but these need not be related.
7. Data storage based on the abstraction of data repository. Abstraction of software modules from the
implementation of data storage technology allows the system administrator to choose the type of data repository,
which is maximally adapted for the purposes of the specific RDWE configuration.
8. Data repositories synchronization. The ability of the off-line operations of the RDWE and data
synchronization with the central data repository. This is important in the absence of a permanent network connection.
This feature implemented with the internal mechanisms for replication of the data repository.
General information model of the Research and Development Workstation Environment
The RDWE class system’s generalized information model represented as a three-tuple composite web service
(CWS) using the revised formalism given in [32]:
𝐶𝑊𝑆 = 〈𝐴𝑊𝑆, 𝐹, 𝐶𝐼𝐸𝑛𝑣〉,
where:
𝐶𝑊𝑆 is the RDWE composite web service;
𝐴𝑊𝑆 = {𝑎𝑤𝑠𝑖|𝑖 = 1,𝑚̅̅ ̅̅ ̅̅ }𝑚∈𝑁 is a set of atomic web services (problem-oriented microservices and FLOSS
applications; personalized FLOSS applications) available for usage. The 𝐴𝑊𝑆 set consists of the problem-oriented
atomic web services 𝑎𝑤𝑠 and each of them can be designed and developed as a microservice or a desktop application,
that allows them to be used as an independent software separately from the RDWE and as its components inside 𝐶𝐼𝐸𝑛𝑣;
𝐹 = 𝐴𝑊𝑆: {𝐶𝑗|𝑗 = 1, 𝑛̅̅ ̅̅̅}
𝑛∈𝑁
is a set of functions, the functional filling-up of the RDWE, each function is the
result of coordination and interaction of the 𝐴𝑊𝑆 elements.
𝐶𝑗 ⊆ 𝐴𝑊𝑆, 𝐶𝑗 = {𝑎𝑤𝑠𝑘|𝑘 ≥ 1, 𝑘 ≤ 𝑚}𝑘∈𝑁 is a subset of atomic web services that are required to implement the
𝑗-th function of 𝐶𝑊𝑆;
𝐶𝐼𝐸𝑛𝑣 = {𝑝𝑟𝑙,𝑚𝑖𝑑, 𝑜𝑠, 𝑐𝑟𝑑, 𝑡𝑦𝑝𝑖𝑐𝑎𝑙𝐹𝐿𝑂𝑆𝑆} is a set of elements (represented as layers) that combine into the
Cloud-integrated Environment (CIE);
𝑝𝑟𝑙 – physical resource layer represents physical hardware and facility resources;
𝑚𝑖𝑑 – middle layer (using in the concepts of cloud service orchestration model [30]) represents resource
abstraction and control layer. It is supposed to use OpenStack software platform;
𝑜𝑠 – operating system layer represents guest operating system. It is supposed to use Ubuntu server with LXDE
(abbreviation for Lightweight X11 Desktop Environment) desktop environment or Xfce desktop environment. Atomic
web services work on the operating system layer – 𝑎𝑤𝑠𝑖 ∈ 𝑜𝑠;
𝑐𝑟𝑑 – coordination component. The 𝑐𝑟𝑑 function is to coordinate atomic web services in 𝐶𝑊𝑆, and by the
coordination procedure we will understand the execution of invocation of some 𝑎𝑤𝑠𝑘 ∈ 𝐶𝑗 in the defined sequence. The
coordination component 𝑐𝑟𝑑 can be implemented as the reverse proxy server of tasks. Nginx also is a part of 𝐶𝑅𝐷 –
used as front-end to control and protect access to the server on a private network, performs tasks such as load-
balancing, authentication, decryption, and caching.
𝑡𝑦𝑝𝑖𝑐𝑎𝑙𝐹𝐿𝑂𝑆𝑆 – a typical FLOSS layer includes some regular application suit needed for the scientific research
and development lifecycle (regular software suit may change in the future): LibreOffice office suite; Mozilla Firefox
and Chromium web browsers; Sylpheed email client; Sublime Text 2 and jEdit source code editors; Wine compatibility
layer that aims to allow computer programs developed for Microsoft Windows to run on Unix-like operating systems;
Python (SciPy Python library used for scientific computing and technical computing); R environment for statistical
computing and graphics; Eclipse integrated development environment; Redmine project management and issue tracking
tool; X2Go remote desktop software.
CIE of RDWE delivers to researchers (to researcher’s client device – laptop, desktop, mobile or tablet) using the
extended Platform-as-a-Service service delivery model via X2Go remote desktop software and ssh cryptographic
network protocol (picture).
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Picture. Cloud-integrated Environment of RDWE delivery model
To take all features of CIE, the researcher’s client device (laptop or desktop) must run latest stable release of
X2Go remote desktop software and comply with the following system requirements.
X2Go Client is part of Ubuntu 12.04 & later, Fedora 19 and later, Raspbian Wheezy & Jessie. X2Go Client is
currently only released as a 32-bit x86 build. Both 32-bit x86 and 64-bit x86 versions of Windows are supported:
Windows XP 32-bit SP3 (deprecated); Windows XP 64-bit SP2 (deprecated); Windows Vista SP2;Windows 7 SP1;
Windows 8.1 (with “Update 1”); Windows 10 (1607).
Connecting to the CIE via ssh, the researcher’s client device (laptop or desktop) must comply with the following
system requirements:
Windows XP 32-bit SP3,64-bit SP2; Vista SP2, 7 SP1, 8.1 (with “Update 1”), 10 (1607) with PuTTY
terminal emulator installed;
Linux;
MacOS 10.9 Maverics and higher were tested.
Software implementation of the RDWE class systems is carried out using software specific to the chosen domain
area and may be different from the one above.
Personal Research Information System – Research and Development Workstation
Environment for ontology engineering and scientific creativity support
The main feature of the RDWE class systems is the problem orientation to an arbitrary domain area. As part of
the research and development work of the Glushkov Institute of Cybernetics of National Academy of Sciences of
Ukraine (Department of Microprocessor Technology) has developed and implemented the software system in its class.
It was called – Personal Research Information System (PRIS) [33] – the RDWE class system for supporting research in
the field of ontology engineering (the automated building of applied ontology in an arbitrary domain area as a main
feature), scientific and technical creativity (the automated preparation of application documents for patenting inventions
in Ukraine as a main feature). In accordance with the fundamental information model of the RDWE systems, the PRIS
information model was developed.
At the actual stage of PRIS system development the 𝐴𝑊𝑆 set consists of the following 𝑎𝑤𝑠 (natural language
processing (NLP) functions are available for the Ukrainian language):
𝑎𝑤𝑠1 – RESTful web service for converting PDF files to plain/text. Available via GitHub repository [34].
𝑎𝑤𝑠2 – RESTful web service for converting DOC/DOCX files to plain/text. Available via GitHub repository
[35].
𝑎𝑤𝑠3 – RESTful web service for language identification.
𝑎𝑤𝑠4 – RESTful web service for automatic plain/text summarization. Available via GitHub repository [36].
𝑎𝑤𝑠5 – RESTful web service for converting plain/text from UTF-8 to WIN-1251 enc. Available via GitHub
repository [37].
𝑎𝑤𝑠6 – RESTful web service for automatic detection of title, author’s names, and page numbers for PDF
files.
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265
𝑎𝑤𝑠7 – RESTful web service for automatic keyword plain/text detection.
𝑎𝑤𝑠8 – RESTful web service for automatic sentence segmentation.
𝑎𝑤𝑠9 – RESTful web service for automatic word segmentation with lemmatization and Part-of-speech
tagging.
𝑎𝑤𝑠10 – RESTful web service for automatic word segmentation.
𝑎𝑤𝑠11 – RESTful web service for Compositional Language Pre-processing (CLP): term segmentation
(dividing a string of written language into multiple-word and one-word terms) with lemmatization and Part-of-
speech tagging.
𝑎𝑤𝑠12 – RESTful web service for automatic “stop words” filtering out. These are words that do not bear the
semantic load.
𝑎𝑤𝑠13 – RESTful web service for automatic word lemmatization.
𝑎𝑤𝑠14 – RESTful web service for syntactic parser SyntaxNet: Neural Models of Syntax: an open-source
neural network framework implemented in TensorFlow that provides a foundation for Natural Language
Understanding (NLU) systems [38 – 40].
𝑎𝑤𝑠15 – RESTful web service for searching scientific publications in external bibliographic databases (an
intelligent agent for the automated search of scientific publications [41]). Google Scholar search implemented via
scholar.py [42] library.
𝑎𝑤𝑠16 – RESTful web service for text indexing and annotating (for full-text search capability).
𝑎𝑤𝑠17 – MongoDB as a service (storing the originals of text documents and texts, ontological structures,
ontologies, processed texts in the form of JSON-documents, neural language vector models (NLVM) – word
embedding models [43 – 44]).
𝑎𝑤𝑠18 – “Graph Editor” – web service (represented as Single Page Application – SPA) for text’s ontological
representation, for manipulating ontologies and ontological structures. “Graph Editor” is a part of “TODOS” [ 45] –
IT-platform formation transdisciplinary information environment.
𝑎𝑤𝑠19 – “CONFOR” [46] – web service (represented as SPA) for intelligent data analysis. The main
functions of “CONFOR” are: revealing the regularities that characterize classes of objects which are represented as
sets of attribute values; using the revealed regularities for classification, diagnostics, and prediction.
𝑎𝑤𝑠20 – “KONSPEKT” [47] – web service (represented as SPA) for syntactic and semantic analysis of
natural language texts.
𝑎𝑤𝑠21 – RESTful web service for processing NLVM of large corpora or open data collections (ODC):
implemented via custom gensim [48] as a service server application. It is possible to: calculate semantic similarity
between pair of terms (including multiple-word terms, one-word terms, words) within the chosen NLVM[49];
compute a list of nearest semantic associates for terms (including multiple-word terms, one-word terms, words)
within the chosen NLVM; find the center of lexical cluster for a set of terms (including multiple -word terms, one-
word terms, words) within the chosen NLVM; calculate semantic similarity between two sets of terms (including
multiple-word terms, one-word terms, words) within the chosen NLVM.
𝑎𝑤𝑠22 – “Personal ontological knowledge base for researcher’s publications” web service [50] (represented as
SPA).
𝑎𝑤𝑠23 – Web service (represented as SPA) for creating and filling out templates that will allow to generate an
incoming flow of documents coming from applicant of invention for industrial property [33].
𝑎𝑤𝑠24 – additional external FLOSS (web services, libraries, utilities, etc.);
The 𝐹 set. Functional filling-up of the RDWE is represented by the following set of synthesized from the set of
AWS functions:
𝐹 = 𝐴𝑊𝑆: {𝐶1, 𝐶2𝐶3, 𝐶4, 𝐶5, 𝐶6, 𝐶7},
where:
𝐶1 – ontology engineering. The automated building of applied ontology in an arbitrary domain area (or
knowledge domain). The fundamental foundations of the implemented technology are described in the works [50 – 55]:
𝐶1 = {𝑎𝑤𝑠1…𝑎𝑤𝑠3, 𝑎𝑤𝑠5 , 𝑎𝑤𝑠6, 𝑎𝑤𝑠8, 𝑎𝑤𝑠9, 𝑎𝑤𝑠11, 𝑎𝑤𝑠12, 𝑎𝑤𝑠14, 𝑎𝑤𝑠16…𝑎𝑤𝑠20};
𝐶2 – the research design of scientific publications in an arbitrary domain area (or knowledge domain) [33, 50]:
𝐶2 = {𝑎𝑤𝑠1 …𝑎𝑤𝑠9, 𝑎𝑤𝑠11…𝑎𝑤𝑠13, 𝑎𝑤𝑠15…𝑎𝑤𝑠18, 𝑎𝑤𝑠22, 𝑎𝑤𝑠24};
𝐶3 – personalized ontological processing of scientific publications [33, 50]:
𝐶3 = {𝑎𝑤𝑠1 …𝑎𝑤𝑠9, 𝑎𝑤𝑠11…𝑎𝑤𝑠13, 𝑎𝑤𝑠15…𝑎𝑤𝑠18, 𝑎𝑤𝑠22};
Інформаційні системи
266
𝐶4 – the automated CLP for training and querying NLVM for large corpora or ODC [33]:
𝐶4 = {𝑎𝑤𝑠1 …𝑎𝑤𝑠9, 𝑎𝑤𝑠5, 𝑎𝑤𝑠8…𝑎𝑤𝑠12, 𝑎𝑤𝑠20, 𝑎𝑤𝑠24};
𝐶5 – the automated distributive and semantic analysis of large corpora or ODC [33]:
𝐶5 = {𝑎𝑤𝑠21, 𝑎𝑤𝑠24};
𝐶6 – the automatic syntactic parsing:
𝐶6 = {𝑎𝑤𝑠1 …𝑎𝑤𝑠3, 𝑎𝑤𝑠8, 𝑎𝑤𝑠14, 𝑎𝑤𝑠20, 𝑎𝑤𝑠24};
𝐶7 – the automated preparation of application documents for patenting inventions in Ukraine. The fundamental
foundations of the implemented technology are described in [33]:
𝐶7 = {𝑎𝑤𝑠1 …𝑎𝑤𝑠3, 𝑎𝑤𝑠21, 𝑎𝑤𝑠23, 𝑎𝑤𝑠24}.
Conclusion
The development of modern technologies increasingly covers the field of intellectual activity and, especially,
in the field of scientific research and development. The existing Current Research Information Systems oriented on
the following main types of services: access and reuse of scientific and academic information, methodologies, and
technologies; information search; targeted dissemination of information; messaging services; bridging of horizontal
and vertical relations between scientists; backup data storage and archival information.
We propose the new class of Current Research Information Systems and related intelligent information
technologies. This class supports the main stages of the scientific research and development lifecycle, starting with
the semantic analysis of the information of arbitrary domain area and ending with the formation of constructive
features of innovative proposals. It was called – Research and Development Workstation Environment – the
comprehensive problem-oriented information systems for scientific research and development support. A distinctive
feature of such systems is the possibility of their problematic orientation to various types of scientific activities by
combining on a variety of functional services and adding new ones within the Cloud-integrated Environment. Taking
into account the objective factor of Ukrainian science state, we define the general principles of the RDWE design:
modular design; microservices-based architecture; cross-platform software (also multi-platform software or
platform-independent software); JSON/XML data model and REST architectural style (RESTful web services);
using free and open-source software; cloud computing; data storage based on the abstraction of data repository; data
repositories synchronization.
The Research and Development Workstation Environment class system’s generalized information model is
represented in the article as a three-tuple composite web service that include: a set of atomic web services, each of
them can be designed and developed as a microservice or a desktop application, that allows them to be used as an
independent software separately; a set of functions, the functional filling-up of the Research and Development
Workstation Environment; a subset of atomic web services that are required to implement function of composite web
service. In accordance with the fundamental information model of the Research and Development Workstation
Environment systems, the Personal Research Information System information model was developed.
The main results of our work are focused on enhancing the effectiveness of the scientist’s research and
development lifecycle in the arbitrary domain area. In the future, it would be interesting to apply to the educational
process the modern paradigm of Service-Oriented Learning implemented as Personal Learning Environments,
Virtual Learning Environments, Learning Management Systems, Education-as-a-Service models, and the new class
of E-learning systems – Cloud Learning Environments – using RDWE systems as a E-learning/education-oriented
environment.
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About the authors:
Oleksandr Palagin,
Doctor of Sciences, Academician of National Academy of Sciences of Ukraine,
Deputy director of Glushkov Institute of Cybernetics,
head of department 205 at Glushkov Institute of Cybernetics,
290 Ukrainian publications,
45 International publications,
H-index: Google Scholar – 15,
Scopus – 3.
http://orcid.org/0000-0003-3223-1391.
Vitalii Velychko,
PhD, assistant professor, Senior researcher,
73 Ukrainian publications,
25 International publications.
H-index: Google Scholar – 7,
Scopus – 1.
http://orcid.org/0000-0002-7155-9202.
Kyrylo Malakhov,
Junior Research Fellow,
32 Ukrainian publications,
2 International publications.
H-index: Google Scholar – 2.
http://orcid.org/0000-0003-3223-9844.
Oleksandr Shchurov,
1 category software engineer,
6 Ukrainian publications.
H-index: Google Scholar – 1.
http://orcid.org/0000-0002-0449-1295.
Affiliation:
Glushkov Institute of Cybernetics of National Academy of Sciences of Ukraine,
40 Glushkov ave., Kyiv, Ukraine, 03187.
Tel.: (+38) (044) 526 3348.
Email: palagin_a@ukr.net,
aduisukr@gmail.com
https://github.com/RaRe-Technologies/gensim
http://orcid.org/0000-0003-3223-1391
http://orcid.org/0000-0002-7155-9202
http://orcid.org/0000-0003-3223-9844
http://orcid.org/0000-0002-0449-1295
mailto:palagin_a@ukr.net
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