[Nrg-l] MineNet CFP

Mark Crovella crovella at cs.bu.edu
Wed Mar 22 16:36:40 EST 2006


Our apologies if you receive multiple copies of this message.


########################################################################
#                                                                      #
#                                                                      #
#                                 CALL FOR PAPERS                      #
#                                                                      #
#        Second ACM SIGCOMM 2006 Workshop on Mining Network Data       #
#                   (MineNet-06)                                       #
#      (to be held with ACM SIGCOMM 2006, Sept 11-15, Pisa, Italy)     #
#                                                                      #
#         http://www.acm.org/sigs/sigcomm/sigcomm2006/minenet          # 
#                                                                      #
########################################################################


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. Rece!
 nt 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.


Submission Instructions

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: (Link TBA). 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.


Important Dates 

Paper Registration Deadline: April 21, 2006, 11.59 PM PST (Pacific Standard Time)

Paper Submission Deadline: April 25, 2006, 11.59 PM PST (Pacific Standard Time)

Notification Deadline: May 29, 2006

Camera Ready Deadline: June 16, 2006

Workshop Date:  September 15, 2006



Workshop Co-Chairs

Subhabrata Sen, AT&T Labs-Research (sen at research.att.com) 

Sambit Sahu, IBM Research (sambits at us.ibm.com)


Program Committee 

Graham Cormode, Lucent Bell Labs

Mark Crovella, Boston University

Michalis Faloutsos, U.C. Riverside

Anja Feldmann, T.U. Munchen

Minos Garofalakis, Intel Research Berkeley

Patrick Haffner, AT&T Research

Hani Jamjoom, IBM Research

Chuanyi Ji, Georgia Institute of Technology

Muthu Muthukrishnan, Rutgers

Konstantina Papagiannaki, Intel Research Cambridge

Matthew Roughan, Univ. of  Adelaide, Australia

 Kave Salamatian, LIP6, France

Dawn Song, Carnegie Mellon Univ.

Oliver Spatscheck, AT&T Research

Patrick Thiran, EPFL Switzerland

ZhiLi Zhang, University of Minnesota







_______________________________________________
Minenet_06_pc mailing list
Minenet_06_pc at research.att.com
http://mailman.research.att.com/mailman/listinfo/minenet_06_pc




More information about the Nrg-l mailing list