For seven years in a row, we are happy to announce the new edition of the workshop. Stream processing and real-time analytics have become some of the most important topics in Big Data. Noticeably, the industry tends to develop more robust, powerful and intelligent stream processing applications. IoT applications, online predictive maintenance, fraud detection for instant payments, scoring of consumers on websites and shops, claims analysis and cost estimates, image processing for surveillance, food, and agriculture, etc. are only a few potential applications of real-time stream processing and analytics.

The recent introduction of stateful stream processing [9,14,16,29] has enabled the development of a new kind of real-time applications. Indeed, hot and cold data have been combined into a single real-time data flow using the concept of Stream Tables [15,16,18, 30]. The concept of duality between Streams and Tables is not recent. It was first introduced in 2003 as a “Relation to Stream” transformation, called STREAM [20]. However, it is only with the emergence of state management [14,31] that Stream Tables can now be used in real-time and in a completely distributed manner while still guaranteeing exactly once semantics and recovery mechanisms [28].

Furthermore, stateful stream processing has been applied in data management using Stream & Complex Event Processing (CEP) or Composite Event Recognition (CER) [20]. New architecture patterns were proposed to resolve data pipelines and data management within the enterprise. For instance, the authors in [11,12] proposed new designs for the Extract, Transform and Load (ETL) steps based on stream processing. Thus, by breaking down silos between Enterprise Data Warehouses (EDW) and Big Data lakes [13], doors have been opened to completely redesign the way data are transported, stored and used within the Big Data environment. More recently, Friedman et al. described in [21] how a Data Hub can be implemented to store and distribute data within an enterprise context.

In the past few years, researchers and practitioners in the area of data stream management and CEP/CER [1, 2, 3, 4, 5] 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 [6]. Highly scalable distributed stream processors, the convergence of batch and stream engines, and the emergence of state management & stateful stream processing (such as Apache Spark [9], Apache Flink [10], Kafka Stream [18, 19], Google dataflow [17], Microsoft Trill [26]) opened up new opportunities for highly scalable and distributed real-time analytics. Going further, these technologies also provide solid-foundation algorithms complementary to the CEP/CER in the use cases required by the industry. As a result, with the stateful nature of stream processors [14], stream SQL statements [27] can be applied directly in the streaming engine and dynamic tables can be created [12, 15, 18].

Besides, formalisms for reasoning on durative events have appeared in the past, and they were introduced for improving CER [22, 23, 24]. This led to the introduction of Stream Reasoning for improving Stream Mining tasks, autonomous cars or drones and many other use cases [32].

For the present workshop, and following the discussion above, submissions studying scalable online learning, incremental learning on stream processing infrastructures, Complex event processing and Composite event recognition are welcomed. We also encourage submissions on data stream management, data architecture using stream processing and the Internet of Things (IoT) data streaming. Additionally, we appreciate submissions studying the usage of stream processing in new innovative architectures.

After the success of the first six editions of this workshop, co-located with the IEEE Big Data since 2016, this last edition will be an excellent opportunity to bring together actors from academia and industry to discuss, explore and define new opportunities and use cases. The workshop will benefit both researchers and practitioners interested in the latest research in real-time and stream processing. It will showcase prototypes or products leveraging big data technologies as well as online learning models, efficient algorithms for scalable CEP/CER and context detection engines, and also new architectures leveraging stream processing.

Finally, as our workshop places emphasis on reproducibility, we also encourage authors to make available all data used for empirical evaluations, the related software as well as clear instructions for reproducing the presented experiments. This can be added as a form of supplementary material. The reviewers will be encouraged to consider this material.

Research Topics

The topics of interest include but are not limited to:


In the past editions, we put an emphasis on finding relevant industrial keynote speakers who can drive the audience to the workshop and to the conference. By inviting key technological influencers in streaming, the workshop became a reference in this domain. As examples, the past editions invited keynote speakers from databricks (Apache Spark), Splunk( Apache Pulsar), Ververica/ Alibaba (Apache Flink), Confluent (Kafka).
This year we have an opportunity to have either Speaker from Google Beam & AISIN (Japan) Connected car.



October 1, 2022
November 1, 2022
November 1, 2022
November 17, 2022
December 17-20, 2022


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


  • Till Rohrmann,
    Ververica/Alibaba, GE/CN
  • Vijay Raghavan
    University of Louisiana, US
  • Raju Gottumukkala
    University of Louisiana, US
  • Jian Chen,
    University of North Alabama, US
  • Nam-Luc Tran,
  • Guido Salvaneschi,
    TU Darmstadt, GE
  • Fabricio Enembreck
    Pontifícia Universidade Católica do Paraná, BR
  • José del Campo Ávila
    Universidad de Málaga, ES
  • Amine Ghrab,
  • Thomas Peel,
    GSK, BE
  • Oscar Romero,
    UPC Barcelona Tecg, ES
  • Hai-Ning Liang,
    Xi’an Jiaotong-Liverpool University, CN