| DC Field | Value | Language |
|---|
| dc.contributor.author | Berko, Andrii | |
| dc.contributor.author | Alieksieiev, Vladyslav | |
| dc.coverage.temporal | 21-25 August 2018, Lviv | |
| dc.date.accessioned | 2020-06-19T12:05:51Z | - |
| dc.date.available | 2020-06-19T12:05:51Z | - |
| dc.date.created | 2018-02-28 | |
| dc.date.issued | 2018-02-28 | |
| dc.identifier.citation | Berko A. A Method to Solve Uncertainty Problem for Big Data Sources / Andrii Berko, Vladyslav Alieksieiev // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 32–37. — (Big Data & Data Science Using Intelligent Approaches). | |
| dc.identifier.isbn | © Національний університет „Львівська політехніка“, 2018 | |
| dc.identifier.isbn | © Національний університет „Львівська політехніка“, 2018 | |
| dc.identifier.uri | https://ena.lpnu.ua/handle/ntb/52532 | - |
| dc.description.abstract | Big Data analysis and processing is a popular
tool for Artificial Intelligence and Data Science based solutions
in various directions of human activity. It is of a great
importance to ensure a reliability and a value of data source.
One of the key problems is the inevitable existence of
uncertainty in stored or missing values. Any uncertainty in a
source causes its disadvantageous, complexity or inapplicability
to use. That is why it is crucial to eliminate uncertainty or to
lower uncertainty influence. Here in this research, we offer
ontology-based method to solve an uncertainty problem for big data sources. | |
| dc.format.extent | 32-37 | |
| dc.language.iso | en | |
| dc.publisher | Lviv Politechnic Publishing House | |
| dc.relation.ispartof | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 | |
| dc.subject | big data | |
| dc.subject | data sources | |
| dc.subject | data uncertainty | |
| dc.subject | ontology | |
| dc.subject | uncertainty elimination | |
| dc.title | A Method to Solve Uncertainty Problem for Big Data Sources | |
| dc.type | Conference Abstract | |
| dc.rights.holder | © Національний університет “Львівська політехніка”, 2018 | |
| dc.contributor.affiliation | Lviv Polytechnic National University | |
| dc.format.pages | 6 | |
| dc.identifier.citationen | Berko A. A Method to Solve Uncertainty Problem for Big Data Sources / Andrii Berko, Vladyslav Alieksieiev // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 32–37. — (Big Data & Data Science Using Intelligent Approaches). | |
| dc.relation.references | [1] C. J. Date, Database in Depth: Relational Theory for Practitioners. O’Reilly, CA, 2005. | |
| dc.relation.references | [2] K. Alieksieieva, and A. Peleshchyshyn, “Application of incomplete and inexact data for commercial web-project management,” Scientific announcements of Lviv Polytechnic National University, Lviv, Ukrane, no. 805, pp.345-353, 2014. | |
| dc.relation.references | [3] N. Marz , and J. Warren, Big Data: Principles and best practices of scalable realtime data systems. Manning Publications, 2015. | |
| dc.relation.references | [4] J. Chen, D. Dosyn, V. Lytvyn, and A. Sachenko, “Smart Data Integration by Goal Driven Ontology Learning. Advances in Big Data,” Advances in Intelligent Systems and Computing, Springer International Publishing AG, pp. 283-292, 2016. | |
| dc.relation.references | [5] D. Losin, Big data analytics. Elsevier Inc., Waltham, MA, USA, 2014. | |
| dc.relation.references | [6] V. Alieksieiev, and O. Gaiduchok “About the problem of data losses in real-time IoT based monitoring systems,” Mathematical Modeling, STUME, Sofia, BULGARIA, Year I, issue 3, pp. 121–122, 2017. | |
| dc.relation.references | [7] V. Alieksieiev, G. Ivasyk, V. Pabyrivskyi, and N. Pabyrivska, “Big data aggregation algorithm for storing obsolete data,” Industry 4.0 – STUME, Sofia, BULGARIA, Year III, issue 1, pp.20–22, 2018. | |
| dc.relation.referencesen | [1] C. J. Date, Database in Depth: Relational Theory for Practitioners. O’Reilly, CA, 2005. | |
| dc.relation.referencesen | [2] K. Alieksieieva, and A. Peleshchyshyn, "Application of incomplete and inexact data for commercial web-project management," Scientific announcements of Lviv Polytechnic National University, Lviv, Ukrane, no. 805, pp.345-353, 2014. | |
| dc.relation.referencesen | [3] N. Marz , and J. Warren, Big Data: Principles and best practices of scalable realtime data systems. Manning Publications, 2015. | |
| dc.relation.referencesen | [4] J. Chen, D. Dosyn, V. Lytvyn, and A. Sachenko, "Smart Data Integration by Goal Driven Ontology Learning. Advances in Big Data," Advances in Intelligent Systems and Computing, Springer International Publishing AG, pp. 283-292, 2016. | |
| dc.relation.referencesen | [5] D. Losin, Big data analytics. Elsevier Inc., Waltham, MA, USA, 2014. | |
| dc.relation.referencesen | [6] V. Alieksieiev, and O. Gaiduchok "About the problem of data losses in real-time IoT based monitoring systems," Mathematical Modeling, STUME, Sofia, BULGARIA, Year I, issue 3, pp. 121–122, 2017. | |
| dc.relation.referencesen | [7] V. Alieksieiev, G. Ivasyk, V. Pabyrivskyi, and N. Pabyrivska, "Big data aggregation algorithm for storing obsolete data," Industry 4.0 – STUME, Sofia, BULGARIA, Year III, issue 1, pp.20–22, 2018. | |
| dc.citation.conference | IEEE second international conference "Data stream mining and processing" | |
| dc.citation.spage | 32 | |
| dc.citation.epage | 37 | |
| dc.coverage.placename | Львів | |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference
|
