BibTeX for a paper by David Kotz at Dartmouth College.
For more information about this paper, visit this web page:
https://www.cs.dartmouth.edu/~kotz/research/li-jfilter/index.html

@Article{li:jfilter,
  author =        {Ming Li and David Kotz},
  title =         {{Group-aware Stream Filtering for Bandwidth-efficient Data Dissemination}},
  journal =       {International Journal of Parallel, Emergent and Distributed Systems (IJPEDS)},
  year =          2008,
  month =         {December},
  volume =        23,
  number =        6,
  pages =         {429--446},
  publisher =     {Taylor \& Francis},
  copyright =     {Taylor \& Francis},
  address =       {London, UK},
  DOI =           {10.1080/17445760801930955},
  URL =           {https://www.cs.dartmouth.edu/~kotz/research/li-jfilter/index.html},
  note =          {Invited paper},
  abstract =      {In this paper we are concerned with disseminating high-volume data streams to many simultaneous applications over a low-bandwidth wireless mesh network. For bandwidth efficiency, we propose a \emph{group-aware stream filtering} approach, used in conjunction with multicasting, that exploits two overlooked, yet important, properties of these applications: 1) many applications can tolerate some degree of ``slack'' in their data quality requirements, and 2) there may exist multiple subsets of the source data satisfying the quality needs of an application. We can thus choose the ``best alternative'' subset for each application to maximize the data overlap within the group to best benefit from multicasting. An evaluation of our prototype implementation shows that group-aware data filtering can save bandwidth with low CPU overhead. We also analyze the key factors that affect its performance, based on testing with heterogeneous filtering requirements.},
}