The Thales approach is structured around three closely interconnected themes: Big data (data management and storage), Big Analytics (processing, enrichment and value creation) and Visual Analytics (exploitation and interactive visualisation of datasets using parallelisable and/or linearisable analysis algorithms to ensure exhaustivity).
credit Thales GroupNever before in the history of the world has so much data been produced in such a short time. Smartphones, social media and mobile devices are responsible for much of it — but so are the ever-increasing numbers of security cameras, satellites, tags, sensors and wireless networks. New ways of storing data and new indexing architectures are needed as a matter of urgency. Under the leadership of the major Internet players (Google, Facebook, Amazon, Twitter, etc.), data storage processes have adapted to the growth in the volumes of data to be stored. NoSQL (Not Only SQL) database management techniques outperform current SQL relational database management solutions and are driving the emergence of a whole new approach to database architectures.
The classification into “data-driven” and “hypothesis-driven” approaches is a fundamental distinction, and one that transcends the typologies and terminology generally found in Big Analytics. Here we are in the realm of supervised and unsupervised learning, genetic algorithms and heuristic searches, artificial neural networks and relational clustering.
Through the CeNTAI laboratory, which is the focal point for the Group's Big data activities, Thales is developing both supervised learning algorithms (regression, decision trees, SVM, standard neural networks, expectation maximization, classification, etc.) and unsupervised learning algorithms (auto-encoders, clustering, copula theory, Markov chains, self-learning networks, etc.).
A generic platform has been set up has been set up at the CeNTAI lab, with a Big data architecture based on NoSQL and Elasticsearch to store and access large volumes of heterogeneous, dynamic data from information systems either in batch mode or real time, and Big Analytics and Visual Analytics algorithms primarily dedicated to data fusion, intelligent queries, type analysis, community detection, anomaly and weak-signal detection and investigation, predictive analysis and decision support.
Through the CeNTAI laboratory, which is the focal point for the Group's Big data activities, Thales is developing both supervised learning algorithms (regression, decision trees, SVM, standard neural networks, expectation maximization, classification, etc.) and unsupervised learning algorithms (auto-encoders, clustering, copula theory, Markov chains, self-learning networks, etc.).
A generic platform has been set up has been set up at the CeNTAI lab, with a Big data architecture based on NoSQL and Elasticsearch to store and access large volumes of heterogeneous, dynamic data from information systems either in batch mode or real time, and Big Analytics and Visual Analytics algorithms primarily dedicated to data fusion, intelligent queries, type analysis, community detection, anomaly and weak-signal detection and investigation, predictive analysis and decision support.
credit Thales GroupRead more: https://www.thalesgroup.com/
