https://oldena.lpnu.ua/handle/ntb/52532| Title: | A Method to Solve Uncertainty Problem for Big Data Sources |
| Authors: | Berko, Andrii Alieksieiev, Vladyslav |
| Affiliation: | Lviv Polytechnic National University |
| Bibliographic description (Ukraine): | 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). |
| Bibliographic description (International): | 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). |
| Is part of: | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 |
| Conference/Event: | IEEE second international conference "Data stream mining and processing" |
| Issue Date: | 28-Feb-2018 |
| Publisher: | Lviv Politechnic Publishing House |
| Place of the edition/event: | Львів |
| Temporal Coverage: | 21-25 August 2018, Lviv |
| Keywords: | big data data sources data uncertainty ontology uncertainty elimination |
| Number of pages: | 6 |
| Page range: | 32-37 |
| Start page: | 32 |
| End page: | 37 |
| 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. |
| URI: | https://ena.lpnu.ua/handle/ntb/52532 |
| ISBN: | © Національний університет „Львівська політехніка“, 2018 © Національний університет „Львівська політехніка“, 2018 |
| Copyright owner: | © Національний університет “Львівська політехніка”, 2018 |
| References (Ukraine): | [1] C. J. Date, Database in Depth: Relational Theory for Practitioners. O’Reilly, CA, 2005. [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. [3] N. Marz , and J. Warren, Big Data: Principles and best practices of scalable realtime data systems. Manning Publications, 2015. [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. [5] D. Losin, Big data analytics. Elsevier Inc., Waltham, MA, USA, 2014. [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. [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. |
| References (International): | [1] C. J. Date, Database in Depth: Relational Theory for Practitioners. O’Reilly, CA, 2005. [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. [3] N. Marz , and J. Warren, Big Data: Principles and best practices of scalable realtime data systems. Manning Publications, 2015. [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. [5] D. Losin, Big data analytics. Elsevier Inc., Waltham, MA, USA, 2014. [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. [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. |
| Content type: | Conference Abstract |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference |
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2018_Berko_A-A_Method_to_Solve_Uncertainty_32-37.pdf | 168.84 kB | Adobe PDF | View/Open | |
| 2018_Berko_A-A_Method_to_Solve_Uncertainty_32-37__COVER.png | 569.24 kB | image/png | View/Open |
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