Seminario Complex Event Processing Rules with Genetic Programming

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El Grupo de Sistemas Inteligentes (GSI), reconocido oficialmente por la Universidad Politécnica de Madrid (UPM), está integrado por 14 profesores, 8 investigadores y 7 estudiantes de grado de diferentes departamentos de la universidad, la mayoría de ellos del Departamento de Ingeniería de Sistemas Telemáticos (DIT) de la Escuela Técnica Superior de Ingenieros de Telecomunicación (ETSIT-UPM).

Desde 1982, nuestro grupo ha llevado a cabo la investigación, el desarrollo y la innovación de teorías, métodos y aplicaciones de técnicas de inteligencia artificial, especialmente en los campos de Aprendizaje automático, Big Data, Tecnologías semánticas y Linked Data, Agentes inteligentes y Simulación social, e Ingeniería web y de servicios.

Nuestro grupo ofrece trabajos fin de grado y trabajos fin de máster en temas relacionados con Aprendizaje Automático, Big Data y la Web de Datos. Ofrecemos la posibilidad de publicar el resultado en congresos internacionales.

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Canal GSI

Seminario Complex Event Processing Rules with Genetic Programming

Cuándo: martes 19 de junio a las 16:00

Dónde: sala B-225 de la ETSIT (UPM).

Ponente: Jürgen Dunkel

Título: Learning Complex Event Processing Rules with Genetic Programming

Resumen: Complex Event Processing (CEP) is an established software technology to extract relevant information from massive data streams. Currently, domain experts have to determine manually CEP rules that define a situation of interest. However, often CEP rules cannot be formulated by experts, because the relevant interdependencies and relations between the data are not explicitly known, but inherently hidden in the data streams. To cope with this problem, we present a new learning approach for CEP rules, which is based on Genetic Programming. We discuss in detail the different building blocks of Genetic Programming and how to adjust them to CEP rule learning. The extensive experiments, with synthetic data as well a real world data, show, that Genetic Programming has a high potential for learning CEP rules. In most of our experiments we could derive CEP rules with a nearly perfect recall and precision.