Data processing technologies for extracting knowledge in the form of patterns that can be applied in financial and market analysis, business innovation, fraud detection, security and privacy, anomalies detection, etc.
Also, when data is provided in text form like medical-historic in text form analysis, web-pages analysis, tweet sentiment analysis.
The increasing storage and processing capabilities of computational systems have led to an unprecedented increase of available data in different application domains, such as e-commerce, marketing, energy efficiency, healthcare, and security. These massive datasets are commonly named Big Data, and they implicitly contain very valuable insights about the monitored processes, users, and organizations. Our research work in this area is focused on the development of cutting-edge Data Mining algorithms to: (1) extract relevant knowledge from Big Data; and (2) to manage information imprecision and uncertainty, which are inherent in most application domains.
Thanks to the evolution in the technology, there is currently more stored data than ever. Those data contain information that represents specific contexts and their evolution along time. Analysing them to understand the tendency and predict it, is a recent and productive application field. Business and Marketing have used this analysis during years to lead good investments, and now, it is the moment to move it to new applications. New fields such as chronic diseases or energy production may use the trend analysis to help to understand how the diseases evolve or how reduce energy costs, respectively.