Workshop colocated with the 2016 IEEE International Conference on Big Data
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 or Apache Flink) 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.
The workshop 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 and efficient algorithms for scalable complex event processors and context detection engines.
References
[1] Abadi, Daniel J et al. \”The Design of the Borealis Stream Processing Engine.\” CIDR 4 Jan. 2005: 277-289.
[2] Abadi, Daniel J et al. \”Aurora: a new model and architecture for data stream management.\” The VLDB Journal—The International Journal on Very Large Data Bases 12.2 (2003): 120-139.
[3] Chandrasekaran, Sirish et al. \”TelegraphCQ: continuous dataflow processing.\” Proceedings of the 2003 ACM SIGMOD international conference on Management of data 9 Jun. 2003: 668-668.
[4] Cugola, Gianpaolo, and Alessandro Margara. \”Complex event processing with T-REX.\” Journal of Systems and Software 85.8 (2012): 1709-1728.
[5] Agrawal, Jagrati et al. \”Efficient pattern matching over event streams.\” Proceedings of the 2008 ACM SIGMOD international conference on Management of data 9 Jun. 2008: 147-160.
[6] Brenna, Lars et al. \”Cayuga: a high-performance event processing engine.\” Proceedings of the 2007 ACM SIGMOD international conference on Management of data 11 Jun. 2007: 1100-1102.]
Research Topics
The topics of interest include but are not limited to:
New stream processing architecture for big data.
Complex Event Processing for big data, pattern matching engines for big data.
Scalable real-time decision algorithms.
Scalable stream processing architecture, algorithms or models.
Stream SQL and other continuous query languages on big data frameworks.
Algorithms for high-speed data stream mining.
On-line/incremental learning on data streams.
Important dates
Paper submission deadline: September 15, 2016
Decision notification: October 15, 2016
Camera-ready submission deadline: November 15, 2016
Submission Instructions
Papers submitted should be written in English conforming to the IEEE Conference Proceedings Format (8.5\” x 11\”, Two-Column). The paper should be submitted through the EasyChair System. The length of the papers should not exceed 6 pages + 2 pages for over length charges.
All accepted papers will be published in the Workshop Proceedings published by the IEEE Computer Society Press. Authors of accepted papers, or at least one of them, are requested to register and present their work at the conference, otherwise their papers will be removed from the digital libraries of IEEE CS after the conference.
Submitting a paper to the workshop means that, if the paper is accepted, at least one author should attend the workshop and present the paper.
Program Chairs
Sabri Skhiri, EURA NOVA, BE
Albert Bifet, Télécom Paris Tech, FR
Alessandro Margara, University Lugano, CH
Program Committee Members
Till Rohrmann, Data Artisans, DE
Adnan Tariq, Universität Stuttgart, DE
Maosong Fu, Twitter, US
Nam-Luc Tran, EURA NOVA, BE
Thomas Peel, EURA NOVA, BE
Guido Salvaneschi, TU Darmstadt, DE
Marwan Hassani, RWTH Aachen University, DE
Fabricio Enembreck, Pontifícia Universidade Católica do Paraná, BR
Cheng He, Huawei, CN
Matteo Migliavacca, University of Kent, UK
José del Campo, UMA, ES