Case Studies on Big Data

Globa, Larysa; Svetsynska, Ievgeniia; Luntovskyy, Andriy

Повний запис метаданих

Поле DC	Значення	Мова
dc.contributor.author	Globa, Larysa	-
dc.contributor.author	Svetsynska, Ievgeniia	-
dc.contributor.author	Luntovskyy, Andriy	-
dc.date.accessioned	2020-10-23T11:32:17Z	-
dc.date.available	2020-10-23T11:32:17Z	-
dc.date.issued	2016	-
dc.identifier.citation	Globa, 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.uri	https://ela.kpi.ua/handle/123456789/36945	-
dc.source	Journal of Theoretical and Applied Computer Science	uk
dc.subject	Big Data	uk
dc.subject	Ontologies	uk
dc.subject	Fuzzy Knowledge Base	uk
dc.subject	Heavy-Weighting and Light-Weighting Approaches	uk
dc.title	Case Studies on Big Data	uk
dc.type	Article	uk
dc.format.pagerange	pp. 41-52	uk
dc.description.abstracten	The 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
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