Introduction

Stream Processing and Real-time analytics have caught the interest of the industry lately. Many use cases are nowadays waiting for relevant and efficient solutions to be developed. Such use cases include event-driven marketing, dynamic network management & optimization, real-time recommendation, context-aware applications and real-time fraud detection.

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 and the convergence of batch and stream engines (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. As a result, we also encourage submissions studying scalable on-line learning and incremental learning on stream processing infrastructure.

Finally, the maturity of the Real-time technologies have seen emerging new architecture patterns in Event-Driven Architecture or in Data architecture that leverage Stream Processing framework [7,8]. In the same way, the Internet of things has seen a lot of interesting use cases in stream architecture and mining. We also encourage submissions studying the usage of stream processing in new innovative architectures.

After the success of the first edition this workshop, co-located with the IEEE Big Data 2016, this second 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.

Research Topics

The topics of interest include but are not limited to:

Programme

Keynote: Apache Flink Streaming: new features expected in 2018.

Data Artisans will present the next features of Apache Flink Streaming in a 20-mins keynote.

Information

IMPORTANT DATES

SUBMISSION DEADLINE
October 1, 2017
DECISION NOTIFICATION
November 1, 2017
CAMERA-READY
SUBMISSION DEADLINE
November 15, 2017

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
    Data Artisans, DE
  • Maosong Fu
    Twitter, US
  • Nam-Luc Tran
    IBA, BE
  • Amine Ghrab
    EURA NOVA, BE
  • Thomas Peel
    EURA NOVA, FR
  • Guido Salvaneschi
    TU Darmstadt, DE
  • Fabricio Enembreck
    Pontifícia Universidade Católica do Paraná, BR
  • Matteo Migliavacca
    University of Kent, UK
  • José del Campo Ávila
    Universidad de Málaga, ES