ECoMMFiT has a lot of experience in the management, storage and processing of high volumes of data. Some of the key research activities of the Group revolved around Experiments and Computational Fluid Dynamics which generate by design a large amount of data. This information includes: imaging data generated by image acquisition hardware (e.g. high resolution and speed cameras), hydrodynamic data collections from a wide variety of flow sensors (velocity, temperature, pressure) and structured data generated by massive computational numerical simulations in High-Performance Computing facilities, both in-house and external.
The Group has expertise in the fields of Big Data and Machine Learning. A subset of artificial intelligence, Machine (or Statistical) Learning (ML) refers to a vast set of tools for modeling and understanding complex datasets. This topic has recently gained a lot of attention due to its capacity to perform data reduction on very large data sets. Statistical learning has become a very hot field in many scientific areas as well as marketing, finance, engineering and business disciplines.
Big Data analysis provides a tool for the characterization of the data and the detection of anomalies/deviations. Combined with Machine Learning techniques, this large volumes of data can be used to build more or less complex mathematical models for (1) predicting responses to perturbations on a large number of predictors - Supervised Learning and (2) classify large sets of data according to predefined features - Unsupervised Learning.
- model the temporal evolution of water usage and identify consumption trends
- detection of abnormal behaviors in the distribution network track and identify leaks and/or fraud events
- implement a customer-tailored warning system for extra and infra-consumption.
- improvement of the billing procedure and fraud detection in an electrical company, or
- implementation of an alert systems for accidental extubation events in Intensive Care Unit (ICU) patients.
- identify patient state patterns that may potentially lead to unplanned extubations in patients under Mechanical Ventilation.
- derive a model to assess the validity of the RoX index as a predictor for successful extubation. This index, defined as the ratio of oxygen saturation as measured by pulse oximetry/FiO2 to respiratory rate, has already been used as a predictor for a successful extubation in patients with acute hypoxemic respiratory failure.