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1
Content available Dataspace architecture and manage its components class projection
Shakhovska N., Bolubash Y.
ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
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2015
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Vol. 4, No 1
87--95
EN
Big Data technology is described. Big data is a popular term used to describe the exponential growth and availability of data, both structured and unstructured. There is constructed dataspace architecture. Dataspace has focused solely – and passionately – on providing unparalleled expertise in business intelligence and data warehousing strategy and implementation. Dataspaces are an abstraction in data management that aims to overcome some of the problems encountered in data integration system. In our case it is block vector for heterogeneous data representation. Traditionally, data integration and data exchange systems have aimed to offer many of the purported services of dataspace systems. Dataspaces can be viewed as a next step in the evolution of data integration architectures, but are distinct from current data integration systems in the following way. Data integration systems require semantic integration before any services can be provided. Hence, although there is not a single schema to which all the data conforms and the data resides in a multitude of host systems, the data integration system knows the precise relationships between the terms used in each schema. As a result, significant up-front effort is required in order to set up a data integration system. For realization of data integration from different sources we used SQL Server Integration Services, SSIS. For developing the portal as an architectural pattern there is used pattern Model-View-Controller (MVC). There is specifics debug operation data space as a complex system. The query translator in Backus/Naur Form is give.
2
Content available The method for detecting plagiarism in a collection of documents
Shakhovska N., Shvorob I
Applied Computer Science
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2015
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Vol. 11, no 3
56--66
EN
The development of the intelligent system for searching for plagiarism by combining two algorithms of searching fuzzy duplicate is considered in this article. This combining contributed to the high computational efficiency. Another advantage of the algorithm is its high efficiency when small-sized documents are compared. The practical use of the algorithm makes it possible to improve the quality of the detection of plagiarism. Also, this algorithm can be used in different systems text search.
3
Content available remote One approach to dataspace modelling
Shakhovska N., Medykovsky M., Byczkowska-Lipinska L.
Przegląd Elektrotechniczny
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2012
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R. 88, nr 3a
49-50
EN
The paper analyses the problems arising in processing the separate information sources and databases. The physical model of dataspace is presented, covering the formalization of search methods of the unstructured, semi-structured and strictly structured data.
PL
Praca analizuje problemy przetwarzania danych pojawiające się podczas pracy z oddzielnych źródeł informacji. Prezentowany jest model fizyczny przestrzeni, nacisk kładący na formalizm metod przeszukiwania różnego rodzaju danych.
4
Content available remote Dataspace class algebraic system for modeling integrated processes
Shakhovska N., Medykovskyj M., Lytvyn V.
Journal of Applied Computer Science
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2012
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Vol. 20, nr 1
69-80
EN
Problems which arise up during work with separate sources with depositories information using and databases are analyzed. There is built dataspace algebraic system.
5
Content available Dataspace support platform for e-Science
Elsayed I., Brezany P.
Computer Science
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2012
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Vol. 13 (1)
49-61
EN
This work intends to provide a data management solution based on the concepts of dataspaces for the large-scale and long-term management of scientific data. Our approach is to semantically enrich the existing relationship among primary and derived data items, and to preserve both relationships and data together within a dataspace to be reused by owners and others. To enable reuse, data must be well preserved. Preservation of scientific data can best be established if the full life cycle of data is addressed. This is challenged by the e-Science life cycle ontology, whose major goal is to trace the semantics about procedures in scientific experiments. We present a theoretical dataspace model for e-Science applications, its implementation within a dataspace support platform and an experimental evaluation on top of two real world application domains.
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