Demystifying Context Awareness

You got it, in this blog we are talking about CEP and Event Stream Processing (ESP). In recent years, we have seen a lot of interest in “context-aware” applications or, if you prefer, detecting in real-time interesting contexts. But, if you look at tech blogs or even at IT vendors you can sometimes see ESP, CEP or pattern matching engines for this kind of applications. So what? If I need to be able to react in Real-time to interesting situations (business wise) by processing Streams of Events which one should I use? Are they the same? If I do complex things when I process my event stream do I really do Complex Event Processing? Then, does it mean that I need a CEP ?” These are really interesting questions that I will try to answer in this post.

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Euranova @GTC14

The GPU Technology Conference is one of those events where cutting-edge technology meets industrial players who are either see an opportunity to invest in the future trends or are able to match actual problems with the new abilities enabled by the technology. It is the place where the recent developments in the domain of GPU computing get presented, along with a showcase of all the challenges that the GPU technology has resolved so far. For a company such as EURA NOVA, this is the place where existing solutions meets new challenges that require innovation.

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Big Data in Health Care

Data reuse in health care is a vast topic, one problematic being the anonymization of the data themselves. This is such a complex problem that it’s usually the one in the spotlights, but it is useful sometimes to remind ourselves why working out the data is needed. Let us imagine briefly that we have solved the matter and reflect only on the opportunities and solutions Big Data technologies bring.

Electronic_stethoscope

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A distributed data mining framework accelerated with graphics processing units

In the context of processing high volumes of data, the recent developments have led to numerous models and frameworks of distributed processing running on clusters of commodity hardware. On the other side, the Graphics Processing Unit (GPU) has seen much enthusiastic development as a device for general-purpose intensive parallel computation. In this paper we propose a framework which combines both approaches and evaluates the relevance of having nodes in a distributed processing cluster that make use of GPU units for further fine-grained parallel processing. We have engineered parallel and distributed versions of two data mining problems, the naive Bayes classifier and the k-means clustering algorithm, to run on the framework and have evaluated the performance gain. Finally, we also discuss the requirements and perspectives of integrating GPUs in a distributed processing cluster, introducing a fully distributed heterogeneous computing cluster.

Nam-Luc Tran, Quentin Dugauthier, and Sabri Skhiri, A Distributed Data Mining Framework Accelerated with Graphics Processing Units, proceedings of the 2013 International Conference on Cloud Computing and Big Data (CloudCom-Asia), FuZhou, China, December 2013.

Click here to access the paper in its preprint form.

TechCrunch Disrupt Europe 2013

TechCrunch-2

Last week Cyrille & François have had the chance to attend the “TechCrunch Disrupt Europe 2013” event in Berlin. This event main topic was the booming new technological and venture opportunities that are materializing for european startups. EURA NOVA’s objectives were to take a look at the “big European picture” and exchange knowledge with brilliant entrepreneurs  from all around the world.
Cyrille & François came back boosted by a lot of inspiring stories summarized in this discussion. Enjoy!

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