Big Data

A huge volume of data is produced every day, from the information provided by social networks (such as Facebook, Instagram, Whatsapp, etc.) or generated by sensors on mobile devices, including Big Data applications like Google Searches. This deluge of data requires evermore computational resources to process the information more quickly.

Since there is a wide range of data sources, the collected datasets have different noise levels, redundancy, and consistency. Carrying out a Big Data analysis is still an arduous task. Moreover, until now, the software infrastructure for Big Data has had features and tools that are insufficient to solve real problems, especially for the analysis of real-time applications.

Multimedia, social networks, and the Internet of Things (IoT) are collecting more and more information, which means that Big Data will have a growing prospect of creating value for businesses and consumers. Big Data aims to analyze the amount of data and find anomalies or patterns. However, it is common to find multiple data in different places since the cost of data transfers for a single site is prohibitive due to size and bandwidth limitations.

Our research seeks to develop real-time systems that produce information from reduced datasets to maintain high accuracy, mainly in IoT environments.