1,678 research outputs found
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 has 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 RDWE 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 RDWE class the 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 was developed. It was
called - Personal Research Information System. 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. 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.Comment: In English, 13 pages, 1 figure, 1 table, added references in Russian.
Published. Prepared for special issue (UkrPROG 2018 conference) of the
scientific journal "Problems of programming" (Founder: National Academy of
Sciences of Ukraine, Institute of Software Systems of NAS Ukraine
Multi-Agent System for Decision Support in Enterprises
Business decisions must rely not only on organisation’s internal data but also on external data from competitors or relevant events. This information can be obtained from the Web but must be integrated with the data in an organisation’s Data Warehouse (DW). In this paper we discuss the agent-based integration approach using ontologies. To enable common understanding of a domain between people and application systems we introduce business rules approach towards ontology management. Because knowledge in organisation’s ontologies is acquired from business users without technical knowledge simple user interface based on ontology restrictions and predefined templates are used. After data from internal DW, Web and business rules are acquired; agent can deduce new knowledge and therefore facilitate decision making process. Tasks like information retrieval from competitors, creating and reviewing OLAP reports are autonomously performed by agents, while business users have control over their execution through knowledge base in ontology. The approach presented in the paper was verified on the case study from the domain of mobile communications with the emphasis on supply and demand of mobile phones and its accessories
Multi-Agent System for Decision Support in Enterprises
Business decisions must rely not only on organisation’s internal data but also on external data from competitors or relevant events. This information can be obtained from the Web but must be integrated with the data in an organisation’s Data Warehouse (DW). In this paper we discuss the agent-based integration approach using ontologies. To enable common understanding of a domain between people and application systems we introduce business rules approach towards ontology management. Because knowledge in organisation’s ontologies is acquired from business users without technical knowledge simple user interface based on ontology restrictions and predefined templates are used. After data from internal DW, Web and business rules are acquired; agent can deduce new knowledge and therefore facilitate decision making process. Tasks like information retrieval from competitors, creating and reviewing OLAP reports are autonomously performed by agents, while business users have control over their execution through knowledge base in ontology. The approach presented in the paper was verified on the case study from the domain of mobile communications with the emphasis on supply and demand of mobile phones and its accessories
Semantic-based technology trend analysis
© 2015 IEEE. Technology trend analysis offers a flexible instrument to understand both opportunity and competition for emerging technologies. Semantic information is used in Science, Technology & Innovation (ST&I) records which makes the technology trend analysis more challenging. This paper proposes a semantic-based approach for technology trend analysis through emphasizing Subject-Action-Object (SAO) structure, It also applies the trend analysis approach to extract technology information and identify and predict the trend of technology development more effectively. An empirical study on Graphene is completed to demonstrate the proposed trend analysis approach
Computer aided innovation
Innovation plays a crucial role in the development and the sustainability of the economy.
Regardless its importance and the numerous national and international programs to
promote and support innovation, it still remains an ad-hoc process based on hand-shaking
and brainstorming. It is expected that Computer Aided Innovation (CAI) will have an
impact similar to the one that Computer Aided Design and Computer Aided Engineering
had to Manufacturing.
In thesis we investigate the notion of CAI and try to collect the essential elements that
might lead to a systematic approach for innovation. Specifically we first review the
available theories and frameworks that have the potential to provide the necessary
background for future CAI systems. Specifically we consider TRIZ, ARIZ, QFD, SIX
SIGMA and few others which they surely have a common objective but so far they seem to
be unrelated from most other aspects. We compare them and try to identify their conceptual
similarities and their common components at the semantic level.
Furthermore, we have systematically collected the available software systems for
Innovation and interrelated them to our theoretical review. We conclude by proposing a
semantic based software platform for CAI and investigate the critical issues concerning its
implementatio
Towards Populating Generalizable Engineering Design Knowledge
Aiming to populate generalizable engineering design knowledge, we propose a
method to extract facts of the form head entity :: relationship :: tail entity
from sentences found in patent documents. These facts could be combined within
and across patent documents to form knowledge graphs that serve as schemes for
representing as well as storing design knowledge. Existing methods in
engineering design literature often utilise a set of predefined relationships
to populate triples that are statistical approximations rather than facts. In
our method, we train a tagger to identify both entities and relationships from
a sentence. Given a pair of entities thus identified, we train another tagger
to identify the relationship tokens that specifically denote the relationship
between the pair. For training these taggers, we manually construct a dataset
of 44,227 sentences and corresponding facts. We also compare the performance of
the method against typically recommended approaches, wherein, we predict the
edges among tokens by pairing the tokens independently and as part of a graph.
We apply our method to sentences found in patents related to fan systems and
build a domain knowledge base. Upon providing an overview of the knowledge
base, we search for solutions relevant to some key issues prevailing in fan
systems. We organize the responses into knowledge graphs and hold a comparative
discussion against the opinions from ChatGPT
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