https://oldena.lpnu.ua/handle/ntb/45491| Title: | Knowledge-based Big Data Cleanup method |
| Authors: | Berko, Andrii |
| Affiliation: | Lviv Polytechnic National University |
| Bibliographic description (Ukraine): | Berko A. Knowledge-based Big Data Cleanup method / Andrii Berko // Computational Linguistics and Intelligent Systems. — Lviv : Lviv Politechnic Publishing House, 2019. — Vol 2 : Proceedings of the 3nd International conference, COLINS 2019. Workshop, Kharkiv, Ukraine, April 18-19, 2019. — P. 14–21. — (Paper presentations). |
| Bibliographic description (International): | Berko A. Knowledge-based Big Data Cleanup method / Andrii Berko // Computational Linguistics and Intelligent Systems. — Lviv Politechnic Publishing House, 2019. — Vol 2 : Proceedings of the 3nd International conference, COLINS 2019. Workshop, Kharkiv, Ukraine, April 18-19, 2019. — P. 14–21. — (Paper presentations). |
| Is part of: | Computational Linguistics and Intelligent Systems (2), 2019 |
| Journal/Collection: | Computational Linguistics and Intelligent Systems |
| Volume: | 2 : Proceedings of the 3nd International conference, COLINS 2019. Workshop, Kharkiv, Ukraine, April 18-19, 2019 |
| Issue Date: | 18-Apr-2019 |
| Publisher: | Lviv Politechnic Publishing House |
| Place of the edition/event: | Lviv |
| Keywords: | Big Data Ontology Knowledge Base Data Cleanup |
| Number of pages: | 8 |
| Page range: | 14-21 |
| Start page: | 14 |
| End page: | 21 |
| Abstract: | Unlike traditional databases, Big Data stored as NoSQL data resources. Therefore such resources are not ready for efficient use in its original form in most cases. It is due to the availability of various kinds of data anomalies. Most of these anomalies are such as data duplication, ambiguity, inaccuracy, contradiction, absence, the incompleteness of data, etc. To eliminate such incorrectness, data source special cleanup procedures are needed. Data cleanup process requires additional information about the composition, content, meaning, and function of this Big Data resource. Using the special knowledge base can provide a resolving of such problem. |
| URI: | https://ena.lpnu.ua/handle/ntb/45491 |
| ISSN: | 2523-4013 |
| Copyright owner: | © 2019 for the individual papers by the papers’ authors. Copying permitted only for private and academic purposes. This volume is published and copyrighted by its editors. |
| URL for reference material: | https://www.w3.org/TR/2015/REC-rdfa-core-20150317(2015 |
| References (International): | 1. Alieksieiev,V., Berko, A.: A method to solve uncertainty problem for big data sources. In: Proceedings of the 2018 IEEE Second International Conference on Data Stream Mining & Processing, DSMP), 32-37(2018) 2. Aliekseyeva, К.,Berko, A.:Quality evaluation of information resources in web-projects. Actual Problems of Economics 136(10), 226-234 (2012) 3. Date, C. J.: Database in Depth: Relational Theory for Practitioners. O’Reilly, CA (2005). 4. Jaya, M. I., Sidi, F., Ishak, I., Affendey, L. S., Jabar, M. A. : A review of data quality research in achieving high data quality within organization. Journal of Theoretical and Applied Information Technology, Vol.95, No 12, 2647-2657 (2017) 5. Marz, N.,Warren, J.: Big Data: Principles and best practices of scalable realtime data systems, Manning Publications (2015) 6. RDFa Core 1.1 - Third Edition. Syntax and processing rules for embedding RDF through attributes. W3C Recommendation, https://www.w3.org/TR/2015/REC-rdfa-core-20150317(2015) 7. Rubinson, С.: Nulls, Three-Valued Logic, and Ambiguity in SQL : Critiquing Date’s Critique. In: SIGMOD Record Vol. 36, No. 4, 137-143(2007) |
| Content type: | Article |
| Appears in Collections: | Computational linguistics and intelligent systems. – 2019 р. |
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