Analyzing Data in Motion at Scale
GreyCat Technology
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.
Core Features of GreyCat
graph processing
Your business data is not isolated but richly interconnected. That’s why GreyCat represents and processes your data as a graph.
big data
GreyCat is built to scale. It allows to analyze graphs with billions of nodes and edges – and this blazingly fast. Choose from a variety of storage options: from in-memory to highly replicated and distributed.
data in motion
Time is flying... and your data too! GreyCat is built from the ground up to analyze continuously evolving streams of data: even millions of measurements per second on commodity hardware.
analytics and machine learning
GreyCat provides natively state-of-the-art machine learning algorithms, like neuronal networks and Gaussian mixture models, to empower your businesses with the most advanced and up-to-date analytics.
what-if
What would happen if… an advanced simulation engine built on top of prediction models and advanced graph processing algorithms allows GreyCat to explore and evaluate different decisions before they are applied.
research
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.
The Research behind GreyCat
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