|
Published Articles >> Table of Contents >> Abstract
21st International Conference on Data Engineering (ICDE'05)
pp. 730-741
Maintaining Implicated Statistics in Constrained Environments
Yannis Sismanis, I.B.M. Almaden Research Center
Nick Roussopoulos, University of Maryland
Full Article Text:
  
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDE.2005.84
Send link to a friend
| Abstract |
|
Aggregated information regarding implicated entities is critical
for online applications like network management, traffic characterization
or identifying patters of resource consumption. Recently
there has been a flurry of research for online aggregation
on streams (like quantiles, hot items, hierarchical heavy hitters)
but surprizingly the problem of summarizing implicated information
in stream data has received no attention. As an example,
consider an IP-network and the implication source →
destination. Flash crowds, — such as those that follow recent
sport events (like the olympics) or seek information regarding
catastrophic events — or denial of service attacks direct a large
volume of traffic from a huge number of sources to a very small
number of destinations. In this paper we present novel randomized
algorithms for monitoring such implications with constraints
in both memory and processing power for environments like network
routers. Our experiments demonstrate several factors of improvements
over straightforward approaches.
|
 Additional Information
|
Citation:
Yannis Sismanis, Nick Roussopoulos,
"Maintaining Implicated Statistics in Constrained Environments,"
icde,
pp. 730-741,
21st International Conference on Data Engineering (ICDE'05),
2005
|
|
