Big-DAMA 2018 : ACM SIGCOMM 2018 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks
Call For Papers
Call for Papers
Big data and machine learning are transforming the world, and the data communication networks domain is not an exception. Network operators, practitioners and researchers have at their reach today a matchless opportunity to ride on the success of the big data wave. The complexity of today networks has dramatically increased in the last few years, making it more important and challenging to design scalable network measurement and analysis techniques and tools. Critical applications such as network monitoring, network security, or dynamic network management require fast mechanisms for on-line analysis of thousands of events per second, as well as efficient techniques for off-line analysis of massive historical data. Besides characterization, making operational sense out of the ever-growing amount of network measurements is becoming a major challenge.
Despite recent major advances of big data analysis frameworks, their application to the network measurements analysis domain remains poorly understood and investigated, and most of the proposed solutions are in-house and difficult to benchmark. Furthermore, machine learning and big data analytic techniques able to characterize, detect, locate and understand complex behaviors and complex systems promise to shed light on this enormous amount of data, but smart and scalable approaches must be conceived to make them applicable to the networking practice. Last but not least, 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 novel contributions in the field of machine learning and big data analytics applied to data communication network analysis, including scalable analytic techniques and frameworks capable of collecting and analyzing both on-line streams and off-line massive datasets, network traffic traces, topological data, and performance measurements. In addition, Big-DAMA looks for novel and out-of-the-box approaches and use cases related to the application of machine learning and big data in Networking. The workshop will allow researchers and practitioners to share their experiences on designing and developing big data applications for networking, to discuss the open issues related to the application of machine learning into 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 machine learning 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 machine learning and big data in the networking domain. The following is a non-exhaustive list of topics:
Big networking data analysis
Machine learning, data mining and big data analytics in networking
Deep learning for networking
Application of reinforced-learning in networking
Data analytics for network measurements mining
Stream-based machine learning for networking
Big data analysis frameworks for network monitoring data
Distributed monitoring architectures for big networking data
Networking-based benchmarks for big data analysis solutions
Learning algorithms and tools for network anomaly detection and security
Network anomaly diagnosis through big networking data
Machine learning and big data analytics for network management
Big networking data integrity and privacy
Big data analytics and visualization for traffic analysis
Research challenges on machine learning and big data analytics for networking
Collection and processing systems for large-scale topology and performance measurements
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. Papers must include authors names and affiliations for single-blind peer reviewing by the PC. Authors of accepted papers are expected to present their papers at the workshop.
Submit your paper at https://sigcomm18big-dama.hotcrp.com/