Case Studies on Big Data

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dc.contributor.authorGloba, Larysa
dc.contributor.authorSvetsynska, Ievgeniia
dc.contributor.authorLuntovskyy, Andriy
dc.date.accessioned2020-10-23T11:32:17Z
dc.date.available2020-10-23T11:32:17Z
dc.date.issued2016
dc.description.abstractenThe main idea of the Data Mining (DM) is nowadays as follows: overcoming of the Big Data problematics is possible under use of" data compression" via their transformation into (fuzzy) knowledge. The “heavy-weighting approaches” involving precise analytical techniques and expensive specialized software are used for this aim. On the other hand, there is the opportunity to solve the Big Data problem under use of some “light-weighting approaches” based on agility: freeware, multipurpose techniques, minimal challenges on the personnel training and competencies! The paper examines the techniques and case studies on the both topics. The “heavy-weighting approaches”(ontologies, knowledge bases, fuzzy logic and fuzzy knowledge bases) are compared to light-weighting one. The existing reference solutions are discussed.uk
dc.format.pagerangepp. 41-52uk
dc.identifier.citationGloba, L. Case Studies on Big Data / Larysa Globa, Ievgeniia Svetsynska, Andriy Luntovskyy // Journal of Theoretical and Applied Computer Science. – 2016. – Vol. 10, No. 2. – pp. 41-52.uk
dc.identifier.urihttps://ela.kpi.ua/handle/123456789/36945
dc.sourceJournal of Theoretical and Applied Computer Scienceuk
dc.subjectBig Datauk
dc.subjectOntologiesuk
dc.subjectFuzzy Knowledge Baseuk
dc.subjectHeavy-Weighting and Light-Weighting Approachesuk
dc.titleCase Studies on Big Datauk
dc.typeArticleuk

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