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With the increasingly widespread presence of often interconnected sensors (accelerometers, RFID tags, GPS, etc.), the volume and complexity of data produced is also increasing. However current data analysis, machine learning and data mining algorithms are unable to process the large volumes of time series data produced by these sensors.
Moreover, the various tools that make it possible to explore this data are often non-centralized and require the implementation of time-consuming processing chains that have no real added value for the user.
To meet this need, CS has launched the IKATS project (Innovative Tool-Kit for Analysing Time Series). This project, carried out in collaboration with the Laboratoire Informatique de Grenoble (LIG), with support from Airbus and EDF R&D, aims to provide a ready-to-use toolkit that gives the user all the necessary software for the handling, exploratory analysis and visualization of large volumes of time series data within a single framework.
The analysis of this data will make it possible to determine essential predictive models, for example in the field of anticipatory maintenance. There are countless potential fields of application. IKATS will provide models making it possible to supervise manufacturing industry production lines (aeronautics, energy, rail), or to help operate hi-tech industrial systems requiring supervision by networks of sensors. The toolkit will also be of interest in fields involving connected and monitored objects: smart buildings, quantified self, telecoms, the military sector, etc. The IKATS project complements solutions offered by CS in the fields of PLM, digital simulation and high-performance data processing.
Big Data Analytics