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KEY ENABLING TECHNOLOGIES

Artificial Intelligence And Computing

The field of artificial intelligence has seen significant progress in recent years. This category includes those technologies which allow a machine or device to acquire and apply knowledge and carry out smart behaviors, thus allowing for the construction of smart systems with different areas of application. To do so, these smart systems require the combination of different technologies, ranging from data generation or collection to data processing with appropriate computing infrastructure, data analysis, automated learning support and decision-making.

DATA GENERATION AND HANDLING TECHNOLOGIES

Technologies to collect or mine data from different sources for data generation/simulation based on mathematical or physical models and for data management or processing.

  • Data mining and automated clustering. This includes technologies for automated data mining, automated classification/grouping and automatically organized data.
  • Unstructured and semi-structured data handling technologies. Technologies to manage unstructured or semi-structured data. This includes natural language processing technologies for different languages as well as algorithms to automatically detect normal and abnormal structures in sets of data.
  • Modelling and simulation. Technologies for modelling and simulation through the use of physical or mathematical models of complex systems and the simulation of their behavior under different parameters.
  • Digital twins technologies. Technologies which combine models with the real-time operation of complex systems or products and which project the inferences obtained from big data sources into models and simulations in real time, allowing the time between “how it was designed” and “how it operates” to be reduced.

 

EFFECTIVE DATA PROCESSING INFRASTRUCTURES AND PLATFORMS

Technological infrastructures for the processing and computing of big data. These infrastructures share common aims such as the efficient management of resources, the provision of quality computing services and the automated distribution of tasks across available computing resources.

  • Cloud computing. Advanced computing infrastructures in the Cloud for the elastic (self-regulated) management of on-demand services or the federation of cloud resources.
  • HPC (grid & supercomputing). New multi-core processors and modelling systems for supercomputers. New grid computing distributed architectures which allow for an increased degree of flexibility.
  • Fog/edge computing. Key enabling technologies to distribute computational processes to sensors, actuators and electronic devices.
  • New data storage technologies. New technologies for data storage that are smaller, more efficient, provide increased space and last longer.

 

BIG DATA ANALYTICS TECHNOLOGIES

Technologies for processing and analyzing large volumes of data to make decisions.

  • Interactive visual analytics of multiple-scale data. Tecnologías avanzadas para la visualización de datos y el análisis interactivo combinando distintos niveles de abstracción o granularidad.
  • Interactive visual analytics of multiple-scale data. Advanced technologies for data visualization and interactive analysis, combining different levels of abstraction or granularity.
  • Real-time analytics technologies. Consists of technologies capable of analyzing data flows in real time. For example, technologies based on event processing or stream processing.
  • Semantic and knowledge-based analysis. Technologies for analysis and decision-making based on semantic technologies and automated reasoning. This includes support systems for decision-making based on intelligent knowledge representation.
  • Advanced optimization technologies. This consists of different types of algorithms for solving computationally complex optimization problems: metaheuristics, simheuristics, mathematical programming and exact approaches, matheuristics.
  • Statistics/predictive technologies (5). Technologies to discover and analyze patterns in sets of data whose past behavior can be used to predict future behavior.
  • High performance data analytics (7) (5). Infrastructures or platforms that combine different technologies (HPC or cloud, real-time analytics, predictive technologies) in order to analyze large volumes of data at a greater speed (greater than a teraflop).

MACHINE LEARNING TECHNOLOGIES

Technologies based on machine learning to make automated or semi-automated (human-in-the-loop) decisions.

  • Predictive/prescriptive analytics technologies. Technologies based on machine learning systems to predict future behavior based on the analysis of past data or the use of model-based simulators.
  • Deep learning. Cognitive technologies based on neural networks to extract knowledge from data and other sources of information, combining new and past knowledge in memory models that imitate the workings of the human brain.
  • Probabilistic computing. Technologies to process and analyze information with uncertainty based on generative probabilistic models and learning. This includes stochastic computing, fuzzy logic and statistical computing as well as machine learning.
  • Clustering, pattern mining and recognition technologies. Technologies for pattern recognition and automatic classification: computer vision, speech recognition, text recognition.
  • Digital companions. A new generation of digital assistants with the ability to analyze the context in which they are used, continuously adjust and improve their interactions, integrate sources of information (sensors, natural language), evaluate and verify information, request additional information and draw conclusions.