Knowledge-based Big Data Cleanup method

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dc.citation.epage21
dc.citation.journalTitleComputational Linguistics and Intelligent Systems
dc.citation.spage14
dc.citation.volume2 : Proceedings of the 3nd International conference, COLINS 2019. Workshop, Kharkiv, Ukraine, April 18-19, 2019
dc.contributor.affiliationLviv Polytechnic National University
dc.contributor.authorBerko, Andrii
dc.coverage.placenameLviv
dc.date.accessioned2019-10-31T13:21:04Z
dc.date.available2019-10-31T13:21:04Z
dc.date.created2019-04-18
dc.date.issued2019-04-18
dc.description.abstractUnlike traditional databases, Big Data stored as NoSQL data resources. Therefore such resources are not ready for efficient use in its original form in most cases. It is due to the availability of various kinds of data anomalies. Most of these anomalies are such as data duplication, ambiguity, inaccuracy, contradiction, absence, the incompleteness of data, etc. To eliminate such incorrectness, data source special cleanup procedures are needed. Data cleanup process requires additional information about the composition, content, meaning, and function of this Big Data resource. Using the special knowledge base can provide a resolving of such problem.
dc.format.extent14-21
dc.format.pages8
dc.identifier.citationBerko A. Knowledge-based Big Data Cleanup method / Andrii Berko // Computational Linguistics and Intelligent Systems. — Lviv : Lviv Politechnic Publishing House, 2019. — Vol 2 : Proceedings of the 3nd International conference, COLINS 2019. Workshop, Kharkiv, Ukraine, April 18-19, 2019. — P. 14–21. — (Paper presentations).
dc.identifier.citationenBerko A. Knowledge-based Big Data Cleanup method / Andrii Berko // Computational Linguistics and Intelligent Systems. — Lviv Politechnic Publishing House, 2019. — Vol 2 : Proceedings of the 3nd International conference, COLINS 2019. Workshop, Kharkiv, Ukraine, April 18-19, 2019. — P. 14–21. — (Paper presentations).
dc.identifier.issn2523-4013
dc.identifier.urihttps://ena.lpnu.ua/handle/ntb/45491
dc.language.isoen
dc.publisherLviv Politechnic Publishing House
dc.relation.ispartofComputational Linguistics and Intelligent Systems (2), 2019
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dc.relation.referencesen5. Marz, N.,Warren, J.: Big Data: Principles and best practices of scalable realtime data systems, Manning Publications (2015)
dc.relation.referencesen6. RDFa Core 1.1 - Third Edition. Syntax and processing rules for embedding RDF through attributes. W3C Recommendation, https://www.w3.org/TR/2015/REC-rdfa-core-20150317(2015)
dc.relation.referencesen7. Rubinson, С.: Nulls, Three-Valued Logic, and Ambiguity in SQL : Critiquing Date’s Critique. In: SIGMOD Record Vol. 36, No. 4, 137-143(2007)
dc.relation.urihttps://www.w3.org/TR/2015/REC-rdfa-core-20150317(2015
dc.rights.holder© 2019 for the individual papers by the papers’ authors. Copying permitted only for private and academic purposes. This volume is published and copyrighted by its editors.
dc.subjectBig Data
dc.subjectOntology
dc.subjectKnowledge Base
dc.subjectData Cleanup
dc.titleKnowledge-based Big Data Cleanup method
dc.typeArticle

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