| DC Field | Value | Language |
|---|
| dc.contributor.author | Kaleichyk, H. M. | - |
| dc.coverage.spatial | Минск | ru_RU |
| dc.date.accessioned | 2023-05-25T07:51:05Z | - |
| dc.date.available | 2023-05-25T07:51:05Z | - |
| dc.date.issued | 2023 | - |
| dc.identifier.citation | Kaleichyk, H. M. Big data analytics: definition, advantages and types / Kaleichyk H. M. // Электронные системы и технологии : сборник материалов 59-й научной конференции аспирантов, магистрантов и студентов БГУИР, Минск, 17–21 апреля 2023 г. / Белорусский государственный университет информатики и радиоэлектроники ; редкол.: Д. В. Лихаческий [и др.]. – Минск, 2023. – С. 1158–1159. | ru_RU |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/51576 | - |
| dc.description.abstract | Big data analytics is responsible for some of the biggest industry advancements in the world today, whether they are used in the government, finance, healthcare, or other sectors. To provide useful insights for businesses, big data analytics applies advanced analytics to sizable collections of both structured and unstructured data. It is widely used to understand what is working and what is not, to improve processes, systems, and profitability, in fields as diverse as health care, education, insurance, artificial intelligence, retail, and manufacturing. | ru_RU |
| dc.language.iso | en | ru_RU |
| dc.publisher | БГУИР | ru_RU |
| dc.subject | материалы конференций | ru_RU |
| dc.subject | Big Data Analytics | ru_RU |
| dc.subject | Descriptive analytics | ru_RU |
| dc.subject | Diagnostics analytics | ru_RU |
| dc.subject | Predictive analytics | ru_RU |
| dc.subject | Prescriptive analytics | ru_RU |
| dc.title | Big data analytics: definition, advantages and types | ru_RU |
| dc.type | Article | ru_RU |
| Appears in Collections: | Электронные системы и технологии : материалы 59-й конференции аспирантов, магистрантов и студентов (2023)
|
