Big data and Artificial Intelligence/Machine Learning (AI/ML) are transforming the world, and the data communication networks domain is not an exception. The complexity of communication networks has dramatically increased in the last few years, making it more important to design scalable network measurement and analysis techniques, as well as data driven approaches for networking. Critical applications such as network monitoring, network security, or dynamic network management require fast and intelligent mechanisms for on-line analysis of thousands of events per second, as well as efficient techniques for off-line analysis of massive historical data. Making operational sense out of the ever-growing amount of network measurements is becoming a major challenge. In addition, the explosion in volume and heterogeneity of data measurements generated across the entire network stack is opening the door to innovative solutions and out-of-the-box ideas to improve current networks, and many other networking applications besides monitoring and analysis are becoming more data and measurements driven than ever.

The Big-DAMA workshop seeks for contributions in the field of AI/ML and big data analytics applied to data communication network analysis, including the analysis of on-line streams and off-line massive datasets, network traffic traces, topological data, performance measurements, and many more. Big-DAMA targets novel and out-of-the-box approaches and use cases related to the application of AI/ML and big data in networking. The workshop will allow researchers and practitioners to discuss the open issues related to the application of AI/ML to networking problems and to share new ideas and techniques for big data analysis in data communication networks.

TOPICS OF INTEREST

We encourage both mature and positioning submissions describing systems, platforms, algorithms and applications addressing all facets of the application of AI/ML and big data to the analysis of data communication networks. We are particularly interesting in disruptive and novel ideas that permit to unleash the power of AI/ML and big data in the networking domain. The following is a non-exhaustive list of topics:

• AI/ML and big data analytics in networking
• Big networking data analysis
• Deep learning for networking
• Application of reinforcement learning in networking
• Transfer learning and explainable AI (XAI) for networking
• AI/ML and big data for network measurements mining
• Stream-based machine learning for networking
• Big data frameworks for network analytics
• AI/ML and big data for security and anomaly detection
• AI/ML and big data analytics for network management
• Big networking data integrity and privacy
• Big data analytics and visualization for traffic analysis
• Research challenges on AI/ML and big data analytics for networking

SUBMISSION INSTRUCTIONS

Submissions must be original, unpublished work, and not under consideration at another conference or journal. Submitted papers must be at most six (6) pages long, including all figures, tables, references, and appendices in two-column 10pt ACM format. Paper formatting should follow the main ACM CoNEXT 2019 conference guidelines, except from anonymity. Papers must include authors names and affiliations for single-blind peer reviewing by the PC. Accepted papers will be published in the ACM Digital Library. Authors of accepted papers are expected to present their papers at the workshop.

Register and submit your paper at https://conext19big-dama.hotcrp.com/

IMPORTANT DATES

• Paper registration deadline: September 9, 2019
• Paper submission deadline: September 16, 2019
• Paper acceptance notifications: October 4, 2019
• Camera ready due: October 13, 2019

ORGANIZING COMMITTEE

• Pedro Casas, AIT Austrian Institute of Technology, Austria
• Marco Fiore, CNR, Italy
• Steve Uhlig, Queen Mary University of London, UK
• Roya Ensafi, University of Michigan, USA
• Pere-Barlet-Ros, UPC Barcelona, Spain
• Luca Vassio, Politecnico di Torino, Italy