Descripción del proyecto
Una plataforma para usar mejor los datos
El desarrollo y la evolución de dispositivos y sensores para el Internet de las Cosas (IdC) han hecho que pasen a ser elementos importantes en muchos sectores de la industria: generan grandes cantidades de datos que se usan, principalmente, para supervisar, pero podrían aportar muchas otras ventajas. Por desgracia, en este campo, no se han producido las innovaciones necesarias como para poder usar eficazmente estas virtudes, así que, en su mayoría, no se están aprovechando. El proyecto financiado con fondos europeos MORE tiene el objetivo de cambiar las cosas y lograr que la industria pueda disfrutar de las prestaciones potenciales de un mejor uso del IdC. Con este fin, los investigadores de MORE desarrollarán una plataforma en la que emplearán innovaciones como la analítica avanzada, la computación en nube y la computación periférica, así como técnicas de modelización para datos de sensores capaces de generar predicciones más exactas y mejores modelos de diagnóstico.
Objetivo
The widespread use of sensor and IoT devices is generating huge volumes of time series data in various industries like finance, energy, factories, medicine, manufacturing and others. Industries use these data for monitoring, but their main potential is still untapped. Existing techniques and software for time series management do not provide tools sufficiently scalable and sophisticated for managing the huge volumes of data or adequate forecasting, prediction and diagnostics.
MORE will create a platform that will address the technical challenges in time series and stream management, focusing on the RES industry. MORE’s platform will introduce an architecture that combines edge computing and cloud computing to be able to guarantee both responsiveness and provide sophisticated analytics simultaneously. This architecture will be combined with the usage of time series summarization techniques, or as we more accurately term them in MORE, modelling techniques for sensor data. Models are any compressed representations that allow the reconstruction of the original data points of a time series (e.g. a linear function) within a known error-bound (possibly zero). This approach has synergies with the edge computing approach, since summarization can be done at the edge, reducing the load in the whole data processing pipeline.
MORE will introduce advanced analytics tools for prediction, forecasting and diagnostics based on two technological directions: machine learning and pattern extraction, with emphasis to motifs, which is the state-of-the-art for time series. MORE will adjust these techniques to work directly on models of data, thus enabling them to scale beyond state-of-the-art. The ability to ingest huge volumes of data will have an important impact to the accuracy of the prediction and diagnostics models.
Ámbito científico
- natural sciencescomputer and information sciencesinternetinternet of things
- natural sciencescomputer and information sciencessoftware
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- natural sciencescomputer and information sciencesdata sciencedata processing
Palabras clave
Programa(s)
Convocatoria de propuestas
Consulte otros proyectos de esta convocatoriaConvocatoria de subcontratación
H2020-ICT-2020-1
Régimen de financiación
RIA - Research and Innovation actionCoordinador
151 25 Maroussi
Grecia