## 3rd ACM CoNEXT Workshop on Big DAta, Machine Learning and Artificial Intelligence for Data Communication Networks (Big-DAMA)

## Workshop Program

Update: The program for the 3rd ACM CoNEXT Workshop on Big-DAMA is posted.

## Publications

Update: The proceedings of the 3rd ACM CoNEXT Workshop on Big-DAMA is posted.

## Call for Submissions

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 Instruction

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/

## Organizing Committee

Name | Affiliation
--- | ---
Alessio Botta | University of Napoli Federico II
Pedro Casas | AIT Austrian Institute of Technology
Roya Ensafi | University of Michigan
Marco Fiore | CNR
Steve Uhlig | Queen Mary University of London
Pere-Barlet-Ros | UPC Barcelona
Luca Vassio | Politecnico di Torino

## TPC Members

Name | Affiliation
--- | ---
Muhammad Shahwaiz Afaqui | Universitat Oberta de Catalunya
Andrea Araldo | Telecom SudParis
Danilo Giordano | Politecnico di Torino
Roberto Gonzalez | NEC Laboratories Europe
Marco Gramaglia | Universidad Carlos III de Madrid
Felix Iglesias | Vienna University of Technology
Hyun-chul Kim | Sangmyung University
Andrea Morichetta | Politecnico di Torino
Federico La Rocca | Universidad de la República
Lorenzo Maggi | Nokia Bell Labs
Francesco Malandrino | CNR-IEIIT
Anna Maria Mandalari | Imperial College London
Jelena Milosevic | Vienna University of Technology
Andra Lutu | Telefonica Research
Ricardo de Oliveira Schmidt | University of Passo Fundo
Philippe Owezarski | LAAS-CNRS
Paul Patras | University of Edinburgh
Dario Rossi | Huawei Technologies
Daniel Sadoc Menasche | Federal University of Rio de Janeiro
Lionel Tabourier | LIP6 – CNRS and UPMC
Vincenzo Sciancalepore | NEC Laboratories Europe