Semantic (Big) Data Analysis: an Extensive Literature Review
| Autor: | Héctor Hiram Guedea Noriega, Francisco Alberto García Sánchez |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
General Computer Science
Computer science business.industry Big data 02 engineering and technology Linked data Data science Variety (cybernetics) Visualization 020204 information systems Business intelligence 0202 electrical engineering electronic engineering information engineering Data analysis Semantic technology 020201 artificial intelligence & image processing Electrical and Electronic Engineering business Semantic Web |
| Zdroj: | IEEE Latin America Transactions. 17:796-806 |
| ISSN: | 1548-0992 |
| Popis: | For many years, companies have exploited the data registered in their everyday operations by their transactional systems to obtain useful information and assist in decision-making. To this end, different data analysis techniques and business intelligence strategies have been applied. In recent years, the increase in the volume of data, along with variety in data and the velocity at which data is being produced, has led to the conception of novel processing mechanisms capable of dealing with such huge amount of data, namely, Big Data. The main difficulties associated with Big Data management are linked to its collection and storage, search, sharing, analysis and visualization. The formal underpinnings of Semantic Web technologies enable the automated processing of data through sophisticated inference and reasoning techniques. Semantic technologies have been successfully applied in a number of scenarios for the integration of heterogeneous data, data analysis at the knowledge level, and visualization of Linked Data. In the last few years, a large number of published research papers have explored the benefits in using semantic technologies in data analysis and Big Data. In this paper, we provide a systematic review of the literature in this research area, highlighting the main benefits obtained by the integration of semantic technologies in data analysis and the most challenging aspects that remain to be addressed. |
| Databáze: | OpenAIRE |
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