Today's IP networks are extensively instrumented for collecting a wealth of different types of data including traffic (e.g., packet or flow level traces), control (e.g., router forwarding tables, BGP and OSPF updates) and management (e.g., fault, SNMP traps) data. The different measurements often exhibit complex interrelationships and their underlying structure can provide a wealth of information for improving our understanding of network problems and facilitate network management and operations.
Suitable methodologies, tools and techniques are needed to process and analyze the vast amount of primarily unstructured measured data and extract structures, relationships, and "higher level knowledge" embedded in it, and use this information to aid network management and operations. An important question is how advances in fields such as data mining, machine learning, and statistics can be brought to bear on this important problem of information mining for network management. Recent research efforts e.g., in anomaly detection, characterization and control are showing the potential of such an inter-disciplinary approach.

The goal of this workshop is to explore new directions in network data mining and root cause analysis techniques and tools for network monitoring, management, and remediation. The workshop will provide a venue for researchers and practitioners from the networking protocols/systems, data mining, machine learning, and statistics communities, to get together and collaboratively approach this problem from their respective vantage points.

The workshop solicits original/position/work-in-progress papers on the application of data mining, machine learning and statistical techniques to solve network management and operation problems such as network reliability and performance, security, traffic engineering and control. Topics of interest include, but are not limited to, the following:

• Collection, storage & access infrastructure: platform instrumentation (e.g. multi-modal, multi-resolution sensors), collection techniques (e.g. event sampling, filtering, aggregation, etc.), storage and access (e.g. retention policy, indexing techniques etc.).
• Network data analytics techniques & tools: network stream mining, network graph mining, micro-clustering, temporal and statistical correlation, causality tracking, machine learning.
• Applications to network operations & management: network problem determination, network reliability and performance, root-cause analysis, security, emerging phenomenon detection (e.g. DDoS, virus/worm, spam etc.), traffic classification.

Of particular interest are (i) new solution techniques as well as applications of existing techniques from data mining, machine learning and statistics to IP network problems, (ii) experiences with the use of such techniques for IP networks, and (iii) open networking problems and challenges that would benefit from the use of such techniques.

Particularly welcomed are papers that bring out interesting and novel ideas at an early stage in their development. Selected papers will be forward-looking, with impact and implications for both operational networks and ongoing or future research.

Papers should be at most 6 pages long, in standard ACM format (single- spaced, double column, at least 10pt font), and in either postscript or pdf format only. Author names, affiliations, contact information, paper title and paper abstract should also be entered in ascii format at the submission website.

Submit papers via the MineNet-06 submission site using EDAS. Quick instructions on how to use Edas are available here.

NOTE: Papers MUST be pre-registered at the submission website by the April 25th deadline (see below)

Papers will be reviewed single blind. Accepted papers will appear in the workshop proceedings. Authors of accepted papers are expected to present their work at the workshop.

For general questions with regard to the workshop, please contact the Minenet Chairs, sen@research.att.com, sambits@us.ibm.com

For submission site maintenance, please contact the Minenet Web Chair at: {jaideep.chandrashekar@intel.com, jaideepcs@gmail.com}