Introduction

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. 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] 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]. 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] that Stream Tables can now be used in real-time and in a completely distributed manner.

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]) 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 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.

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 four editions of this workshop, co-located with the IEEE Big Data 2016 & 2017 & 2018 & 2019, this fifth 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 models, efficient algorithms for scalable CEP/CER and context detection engines, and also new architectures leveraging stream processing.

Possible Extended Versions in a Peer-Reviewed Journal

The accepted papers will have the opportunity to submit an extended version of their workshop paper in a Special Edition of the MDPI Data Journal . Data (ISSN 2306-5729) is a peer-reviewed open access journal on data in science, with the aim of enhancing data transparency and reusability. The journal is now included in the Emerging Sources Citation Index - ESCI (Web of Science), Scopus and Inspec (IET).

Research Topics

The topics of interest include but are not limited to:

Keynotes

Keynote 1: Apache Flink new feature and outlook for 2021

Keynote 2: Apache Spark & Databrick Delta for data management

Information

IMPORTANT DATES

SUBMISSION DEADLINE
October 1, 2020
DECISION NOTIFICATION
November 1, 2020
CAMERA-READY
SUBMISSION DEADLINE
November 15, 2020
Workshop
December 10-13, 2020

PUBLICATIONS

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

SUBMIT PAPER

PROGRAM CO-CHAIRS

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

PROGRAM COMMITTEE MEMBERS

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