| layout | title | group | dates | committees | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
default |
ACM SIGCOMM 2017 Workshop on Big Data Analytics and Machine Learning for Data Communication Networks (Big-DAMA 2017) |
Workshops |
|
|
Big data is transforming the world, and the data communication networks domain is not an exception. While the success of big data and machine learning in data communication networks has been so far mastered by the big players of the Internet - such as Google, network operators, practitioners and researchers have at their reach a matchless opportunity to also 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 online analysis of thousands of events per second, as well as efficient techniques for offline 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 online streams and offline 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.
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 interested 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
- 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
[Contact workshop co-chairs](mailto:[email protected],[email protected],[email protected],[email protected]?subject=[Big-DAMA 2017]){: data-role="button" class="button" }.
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.
Please submit your paper via https://sigcomm17big-dama.hotcrp.com/.
{% include dates2.html dates=page.dates %}
The official publication date is the date the proceedings are made available in the ACM Digital Library. This date may be up to TWO WEEKS prior to the first day of your conference. The official publication date affects the deadline for any patent filings related to published work.
{% include committees.html committees=page.committees %}