Modern analytics solutions succeed to understand and predict phenomenons in a large diversity of software systems, from social networks to Internet-of-Things platforms. This success challenges analytics algorithms to deal with more and more complex data, which often heavily evolve over time. GreyCat emerges after several years of research to efficiently analyze large-scale data in live. It is designed as a toolset to develop customized operational decision-making tools, able to run on big data centers but also on commodity hardware.
As former researchers, we continuously look at the most advanced methods to solve your challenges. We keep a very strong connection to research institutions around the world.
Analyzing Complex Data in Motion at Scale with Temporal Graphs: T. Hartmann, F. Fouquet, M. Jimenez, R. Rouvoy, Y. Le Traon
Reasoning at runtime using time-distorted contexts: A models@ run. time based approach: T. Hartmann, F. Fouquet, G. Nain, B. Morin, J. Klein, Y. Le Traon
A native versioning concept to support historized models at runtime: T. Hartmann, F. Fouquet, G. Nain, B. Morin, J. Klein, O. Barais, Y. Le Traon
Stream my models: Reactive peer-to-peer distributed models@ run.time: T. Hartmann, A. Moawad, F. Fouquet, G. Nain, J. Klein, Y. Le Traon
Beyond discrete modeling: A continuous and efficient model for IoT: A. Moawad, T. Hartmann, F. Fouquet, G. Nain, J. Klein, Y. Le Traon
Polymer: A Model-Driven Approach for Simpler, Safer, and Evolutive Multi-Objective Optimization Development: A. Moawad, T. Hartmann, F. Fouquet, G. Nain, J. Klein, J. Bourcier
Enabling Model-Driven Live Analytics For Cyber-Physical Systems: The Case of Smart Grids: T. Hartmann
Model-Driven Analytics: Connecting Data, Domain Knowledge, and Learning: T. Hartmann, A. Moawad, F. Fouquet, G. Nain, J. Klein, Y. Le Traon, J.M. Jezequel