Skip to content

EURA NOVA R&D has a new rallying cry : Join The Pack!

Screenshot from 2016-07-26 17-35-03

 

After launching our first bootcamp, we are organising our first workshop colocated with IEEE conference. The workshop will take place in December in Washington D.C. and will bring together industrial and academic stakeholders to discuss, explore and refine new opportunities and use cases in the area of stream processing and real-time analytics in big data.

Indeed, 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.

The workshop will showcase prototypes or products leveraging big data technologies as well as models and efficient algorithms for scalable complex event processors and context detection engines. Here is a short list of research topics to inspire you :

  • New stream processing architecture for big data.
  • Complex event processing for big data, pattern matching engines for big data.
  • Scalable real-time decision algorithms.
  • Scalable stream processing architecture, algorithms or models.
  • Stream SQL and other continuous query languages on big data frameworks.
  • Algorithms for high-speed data stream mining.
  • On-line/incremental learning on data streams.

Your paper will be reviewed by a panel of academic as well as industrial experts.  

Find more information about program co-chairs and members on the workshop website and submit your paper to join the Euranovian pack!

Don’t miss the chance to be part of an IEEE conference and to see Washington under the snow.

 

 

Releated Posts

Evaluation of GraphRAG Strategies for Efficient Information Retrieval

Traditional RAG systems struggle to capture relationships and cross-references between different sources unless explicitly mentioned. This challenge is common in real-world scenarios, where information is often distributed and interlinked, making graphs a more effective representation. Our work provides a technical contribution through a comparative evaluation of retrieval strategies within GraphRAG, focusing on context relevance rather than abstract metrics. We aim to offer practitioners actionable insights into the retrieval component of the GraphRAG pipeline.
Read More

Flight Load Factor Predictions based on Analysis of Ticket Prices and other Factors

The ability to forecast traffic and to size the operation accordingly is a determining factor, for airports. However, to realise its full potential, it needs to be considered as part of a holistic approach, closely linked to airport planning and operations. To ensure airport resources are used efficiently, accurate information about passenger numbers and their effects on the operation is essential. Therefore, this study explores machine learning capabilities enabling predictions of aircraft load factors.
Read More