- Contact person: Manolis Terrovitis
- Relevant publications
- Start date: 01-10-2020
- Duration: 36 months
- Programme: H2020-ICT-2018-20 / H2020-ICT-2020-1
- Funding: 3,7 MEuro
- IMSI funding: 886 KEuro
- Project webpage: http://www.more2020.eu
- Partners: ATHENA Research Center,AALBORG UNIVERSITET, INACCESS NETWORKS S.A. , IBM IRELAND LIMITED, PERCEPTION DYNAMICS LIMITED, BELGISCH LABORATORIUM VAN DEELEKTRICITEITSINDUSTRIE LABORELEC CVBA , MODELARDATA IVS
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.