DataThings

Next-Gen Live Analytics, Crafted in Luxembourg

What we do

about our mission

"Driven by emerging technologies, like the Internet of Things, cyber-physical systems, and the so-called Industry 4.0, our businesses are becoming more and more complex every day. This makes taking sustainable operational decisions hard, especially when they need to be taken in live. At DataThings we believe that the large amount of data collected today from various sources has the potential to help you to better understand your businesses.

Our technology can empower a wide range of fields: health care, transportation systems, building management, Internet of Things, financial sector and nearly every other business that deals with live data. DataThings mission is to develop intelligent software systems to transform your data into actionable and valuable insights to support you in taking the best operational decisions – even in live."

gregory, assaad, thomas, francois

AI & Machine Learning 100%

Software Engineering 100%

Scalable Data Analytics 100%

Temporal Graph Processing 100%

now choose one of our

Available services

  • GreyCat Enterprise: Our flagship product for live data analytics. Built on top of the open source framework GreyCat, it is the result of more than four years of research. Get a license and empower your own analytics platform, or benefit from our know-how to build your next generation analytics software on top of GreayCat Enterprise.
  • Custom/Individual Software Development: Looking for a software solution related to data analytics, machine learning, and/or big data visualization tailored to your needs? How can we support you?
  • Consultancy: Do you need help in Software Engineering, Machine Learning, or Data Analytics? Let’s talk about it.

Our solution

The GreyCat Technology

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. Upon several years of successful cooperation at the University of Luxembourg, the co-founders of DataThings and creators of the GreyCat open source framework decided that it is time for the next chapter: GreyCat Enterprise.

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.

GreyCat

Open Source
  • Temporal Data Support
  • What-If Simulation
  • Isomorphic Java/JS SDK
  • Live Machine Learning
  • Client/Server Stream Protocol
  • Scalable Single Machine Storage
  • Community Support

GreyCat

Enterprise
  • Advanced and GPU-based Machine Learning
  • Distributed, Replicated Storage
  • Secure Encrypted Stream Protocol
  • Access Control
  • Multi-tenant Storage
  • Enterprise Stream Connector for SQL and XML
  • Professional Support
Made in Luxembourg
hear them out

the press

Des données pleines d'utilité

November 10, 2017 - Le Quotidien

"Produit dérivé" du SnT et de l'université du Luxembourg, la start-up a mis au point une technologie permettant de faire de l'ananlyse de données et de l'apprentissage.

They put their trust in us

Our Clients

     let's do it then ..    
get to know our

team members

We are a bunch of passionate computer science researchers and software engineers. After several years of successful collaboration at the University of Luxembourg/SnT, the co-founders of DataThings set out to start the next chapter in data analytics. At DataThings, we aim at making the vision of live analytics of complex and time-evolving data a reality in order to support your businesses in operational decision-making.

From left to right, Max Tricoire, Cyril Cecchinel, Matthieu Jimenez, Assaad Moawad (co-founder), Thomas Hartmann (co-founder), Gregory Nain (co-founder), and Francois Fouquet (co-founder). Photographer: Sebastien Wiertz. Artist: Eric Mangen.

team member
We are hiring

join the team

Are you passionate about machine learning, AI, and software engineering? Do you like to code and work on hard problems? Are you determined and motivated to find the best possible solutions? And above all you are a great team player? We are looking forward to hearing from you!

posts from

our blog

Meta-Learning: Learning to Learn

Feburary 6, 2019

Although artificial intelligence and machine learning are currently extremely fashionable, applying machine learning on real-life problems remains very challenging. Data scientists need to evaluate various learning algorithms and tune their numerous parameters, based on their assumptions and experience, against concrete problems and training data sets. This is a long, tedious, and resource expensive task. Meta-learning is a recent technique to overcome, i.e. automate this problem. Meta-learning aims at using machine learning itself to automatically learn the most appropriate algorithms and parameters for a machine learning algorithm.

Neural networks fudging the numbers

October 29, 2018

The whole purpose of supervised neural networks is to find the best parameters of a certain model that can successfully map some inputs to some outputs. Although neural networks can be impressive, even to the limit of being magical, there is really no magic behind the learning mechanism itself. It usually involves specifying a loss function that acts as a punishment metric and an optimizer that tries its best to reduce the losses. In a previous blog post, we explained all these concepts in details, the general mechanics of neural networks and the details of each of its specific gear.
go to the blog

Our Supporters

wanna say hello?

contact us

Phone: (+352) 20 60 03 15

5, rue de l'industrie, Luxembourg,
L-1811, LUXEMBOURG

contact@datathings.com

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