![[logo] ENX. Workshop](img/logo-enx-workshop.svg){kind=logo}
Stream Processing and Real-time analytics has become one of the most important topic in Big Data. The emergence of new business cases have created a need to develop more robust, more performant, more intelligent Stream Processing applications and analytics. Today, we can find use cases in various industries such as Bank with real-time fraud detection, real time credit risk scoring; Insurance with real-time claim quotation, telematics; Retails with real-time marketing; Telecom with adaptive SLA in network, real-time customer care and many other examples.
These last two years, we have seen arriving another usage of Stream & complex event processing: the data management. New architecture patterns have been proposed to resolve data pipeline and data management within enterprise. In [11,12], the authors describe a way to redesign ETL (Extract Transform and Load) using Stream processing.This opened the door to completely redesign the way the data are transported, stored and used within Big Data environment by breaking down silos between EDW and Big Data lake as shown by [13].
In the past years, researchers and practitioners in the area of data stream management [1, 2, 3] and Complex Event Processing (CEP) [4, 5, 6] have developed systems to process unbounded streams of data and quickly detect situations of interest.
Nowadays, big data technologies provide a new ecosystem to foster research in this area. Highly scalable distributed stream processors, the convergence of batch and stream engines and the emergence of state management & statull stream processing (such as Apache Spark [9] or Apache Flink [10]) open new doors for highly scalable and distributed real-time analytics. Going further, those technologies also provide a solid foundation for real-time analytics algorithms that are complementary to the CEP in the use cases required by the industry. Finally, the Stateful nature of Stream Processors [14] allows to apply Stream SQL statement directly in the streaming engine and creating Dynamic tables [16 ,12].
As a result, we encourage submissions studying scalable online learning and incremental learning on stream processing infrastructure. In addition, we also encourage submissions on Data Stream management, data architectures using Stream processing and Internet of Things data streaming, Finally, we also encourage submissions studying the usage of stream processing in new innovative architectures.
After the success of the first and the second edition, this workshop, co-located with the IEEE Big Data 2016 & 2017, this third edition is an excellent opportunity to gather together actors from academia and industry to discuss, to explore and to refine new opportunities and use cases in the area. The workshop will benefit to both researchers and practitioners interested in the latest researches in real-time and stream processing. The workshop will showcase prototypes or 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.