Update: The program for the 3rd ACM CoNEXT Workshop on Big-DAMA is posted.
Update: The proceedings of the 3rd ACM CoNEXT Workshop on Big-DAMA is posted.
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.
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:
Submissions must be original, unpublished work, and not under consideration at another conference or journal.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/
| 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 |
| 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 |