"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."
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.
Your business data is not isolated but richly interconnected. That’s why GreyCat represents and processes your data as a graph.
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.
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.
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 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.
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.
We are four passionate researchers holding PhDs in Computer Science. After several years of successful collaboration at the University of Luxembourg/SnT, we founded DataThings to start the next chapter in data analytics. 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.
LSTM Neural Networks, which stand for Long Short-Term Memory, are a particular type of recurrent neural networks that got lot of attention recently within the machine learning community. In a simple way, LSTM networks have some internal contextual state cells that act as long-term or short-term memory cells. The output of the LSTM network is modulated by the state of these cells.
Any complex system can be abstracted in a simple way, or at least dissected to its basic abstract components. Complexity arises by the accumulation of several simple layers. The goal of this post, is to explain how neural networks work with the most simple abstraction.
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