Knowledge Engineering and Data Analysis

The group specialises in predictive models based on large volumes of data. It applies different Machine Learning techniques, such as Classification, Regression, Clustering and Dimensionality Reduction, to particularly complex problems.

Algorithms for data processing incorporating AI techniques, to be integrated into business management software applications.

How could the main challenges faced by companies be addressed?

• Limitations in the efficient management of large volumes of data

Big Data environments require the application of new data analysis techniques such as those described above.

• Difficulties in handling unstructured information

The integration of data from different sources produces unstructured repositories that require ETL processes incorporating clustering techniques to organise the information before it is strategically exploited.

• Inefficient predictive models in information-intensive environments

Traditional predictive models do not perform effectively in today’s Big Data environments, making it necessary to apply new algorithms incorporating Artificial Intelligence techniques such as decision trees and pattern extraction.

• Limited ability to adapt Information Systems to changing environments

If the business environment is constantly changing, the predictive models used must also adapt. Data Streaming techniques are highly effective in these cases.