Semantic (Big) Data Analysis: an Extensive Literature Review - Guedea Noriega, Hector H.; Garcia Sanchez, Francisco (Revista IEEE América Latina, 2019)
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.