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 [19] as the concept of “Relation to Stream” transformation. However, this is only with the emergence of state management [14] 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 [13]. In [20] 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 [9], Apache Flink [10], Kafka Stream [18]) 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 [14], 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.

Research Topics

The topics of interest include but are not limited to:


To Be Announced


To Be Announced



October 1, 2019
November 1, 2019
November 15, 2019


Your paper should be written in English and formatted to IEEE Computer Society Proceedings Manuscript Formatting Guidelines (Templates). The length of the paper should not exceed 6 pages.

All accepted papers will be published in the Workshop Proceedings by the IEEE Computer Society Press



  • Sabri Skhiri
  • Albert Bifet
    Télécom Paris Tech, FR
  • Alessandro Margara
    Politecnico di Milano, IT


  • Amine Ghrab
  • Fabian Hüske
    Data Artisans, DE
  • Fabricio Enembreck
    Pontifícia Universidade
    Católica do Paraná, BR
  • Guido Salvaneschi
    TU Darmstadt, DE
  • Hai-Ning Liang
    Xi’an Jiaotong-Liverpool University, CN
  • Jian Chen
    University of North Alabama, US
  • José del Campo Ávila
    Universidad de Málaga, ES
  • Nam-Luc Tran
  • Oscar Romero
    UPC Barcelona, ES
  • Peter Beling
    University of Virginia, US
  • Raju Gottumukkala
    University of Louisiana,US
  • Thomas Peel
  • Vijay Raghavan
    University of Louisiana, US