Skip to content

Second Workshop on Real-Time and Stream Analytics in Big Data

EURA NOVA is thrilled to share the news with you: we are organizing our second workshop collocated with the 2017 IEEE International Conference on Big Data. The workshop will take place in December in Boston, MA, USA.

 

Stream processing and real-time analytics have caught the interest of the industry lately. Many use cases are waiting for relevant and efficient solutions to be developed. Such use cases include event-driven marketing, dynamic network management & optimization, real-time recommendation, context-aware applications and real-time fraud detection.

 

After the success of the first edition, this is an excellent opportunity to bring together the industry and academics  to discuss, to explore and to refine new opportunities and use cases in the area. The workshop will benefit  both researchers and practitioners interested in the latest research in real-time and stream processing. The workshop will showcase prototypes and products leveraging big data technologies as well as models, efficient algorithms for scalable complex event processors and context detection engines, or new architecture leveraging stream processing.
Want to submit a paper? Check out the workshop website to find all the information you  will need. Your paper will be reviewed by a prestigious panel of international experts from both the academic and the industrial worlds.

Releated Posts

Calibrate to Interpret

Trustworthy machine learning is driving a large number of the ML community works in order to improve ML acceptance and adoption. In this paper, we show a first link between uncertainty and explainability, by studying the relation between calibration and interpretation.
Read More

Mass Estimation of Planck Galaxy Clusters using Deep Learning

Galaxy cluster masses can be inferred indirectly using measurements from X-ray band, Sunyaev-Zeldovich (SZ) effect signal or optical observations. Unfortunately, all of them are affected by some bias. Alternatively, we provide an independent estimation of the cluster masses from the Planck PSZ2 catalogue of galaxy clusters using a machine-learning method.
Read More