Case Studies on Big Data

• Autorzy: Globa L. , Svetsynska I. , Luntovskyy A.
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

big data; Ontologies; Fuzzy Knowledge Base; Heavy-Weighting and Light-Weighting Approaches

• Wydawca: Komisja Informatyki Polskiej Akademii Nauk, Oddział w Gdańsku
• Czasopismo: Journal of Theoretical and Applied Computer Science
• Rocznik: 2016
• Tom: Vol. 10, nr 2
• Strony: 41--52
• Opis fizyczny: Bibliogr. 17 poz., rys.

Twórcy

autor

Globa L.

• Institute of Telecommunication Systems, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute", Ukraine

autor

Svetsynska I.

• Institute of Telecommunication Systems, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, Ukraine

autor

Luntovskyy A.

• BA Dresden University of Cooperative Education, Saxon Academy of Studies, Germany

Bibliografia

• [1] Ulema M.: Big Data and Telecommunications Telecom Analytics – Tutorial II, BlackSeaCom’2016.
• [2] Gartner Inc. IT Consulting and Reports (online 2017): http://www.gartner.com/.
• [3] Keberle N.: Modeling of dynamic domains under use of the ontologies, Bulletin of Kharkiv Air Force University, vol. 3, 2009, pp. 121-127.
• [4] Luntovskyy A., Spillner J.: Architectural Transformations in Network Services and Distributed Systems: Service Vision. Case Studies, XXIV, 344p., 238 pict. , Springer Nature Verlag, April 2017 (ISBN: 9-783-6581-484-09).
• [5] Konys A., Rogoza W.: Big Data and Ontologies. Talk at ACS Int. Conf. 2016 in Międzyzdroje, Oct. 2016, 3 p.
• [6] Kuiler E.: From Big Data to Knowledge: An Ontological Approach to Big Data Analytics, Review of Policy Research, Volume 31, Number 4 (2014).
• [7] Globa L., Svetsynska I., Volvach I.: Integral Quality Index of Providing Services Calculation, Conference 2017, 6 p.
• [8] Globa L., Zacharchuk A., Ischenko I.: Expert system for decision-making on the basis of fuzzy logic algorithm, ACS 2016 Conference 2016, 12 p.
• [9] Globa L., Novogrudska R.: Workflow of Ukrainian National Antarctic Scientific Centre Portal Modeling, Ukrainian Antarctic Journal, pp. 229-237, Antarctic, 2016.
• [10] Ivanytska N., Stryzhak O.: Role of Ontology in the System of Formation of Educational and Cognitive Competences on Physics of Secondary School Pupils, in: Information Technologies and Learning Tools, vol. 39 (1), 2016, pp. 160-169.
• [11] Stryzhak O., Globa L., Kovalskyi M.: Increasing web services discovery relevancy in the multiontological environment, in: Advances in Intelligent and Soft Computing Serices (AISC), Springer, 2014, 5 p.
• [12] Russell S., Norvig P.: Artificial Intelligence: A modern approach, New Jersey, Upper Saddle River, 2010.
• [13] Jones M.: Artificial Intelligence: A Systems Approach, Hingham, Massachusetts, New Delhi, Infinity Sci. press LLC, 2008.
• [14] Kriesel D.: A Brief Introduction to Neural Networks (online 2017), http://www.dkriesel.com/en/science/neural_networks.
• [15] Marr B., Wiley J.: Big Data: Using SMART Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance, Sons Ltd, 2015.
• [16] Vortraege der Hausmesse des IBH 2017 0n 23.3.2017 (in German).
• [17] TIQ Solutions Leipzig (online 2017): https://www.tiq-solutions.de.

Uwagi

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).