Stream Processing and Real-time analytics have become one of the most important topics in Big Data. We have seen an important demand from the industry for developing more robust, more powerful and more intelligent stream processing & applications. Banks have deployed in production real-time fraud detection for instant payments. Marketing departments have deployed real-time scoring of consumers on web sites and even in shops. The insurance industry is going even further with claim analysis and real-time cost estimates. Image processing is now available in real time with many applications in security, military battlefield surveillance, food & agriculture. The list goes on.
The recent introduction of stateful stream processing [9, 14,17] allowed to develop a new kind of real-time applications. It enabled to combine hot and cold data into a single Real-Time data flow using the concept of Stream Tables [17, 16]. Interestingly, the concept of duality between Stream and Table is not new, it has already been introduced in STREAM  as the concept of “Relation to Stream” transformation. However, this is only with the emergence of state management  that these concepts are now usable in real-time and in a completely distributed manner as Stream Tables.
Stateful stream processing enabled a second interesting usage of Stream & complex event processing: data management. New architecture patterns have been proposed to resolve data pipelines and data management within the 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 lakes as shown by . In  Gartner describes how a Data Hub can be implemented to store and distribute data within an enterprise context.
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 , Apache Flink , Kafka Stream ) 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, with the Stateful nature of Stream Processors , apply Stream SQL statements can be applied directly in the streaming engine and Dynamic tables can be created [12, 16, 17].
As a result, we encourage submissions studying scalable online learning and incremental learning on stream processing infrastructures. In addition, we also encourage submissions on Data Stream management, data architecture using Stream processing and the 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 three editions of this workshop, co-located with the IEEE Big Data 2016 & 2017 & 2018, this fourth edition is an excellent opportunity to bring together actors from academia and industry to discuss, to explore and to define 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 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.