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-ijcnds/index.html

@Article{li:ijcnds,
  author =        {Ming Li and David Kotz},
  title =         {{Towards Collaborative Data Reduction in Stream-Processing Systems}},
  journal =       {International Journal of Communication Networks and Distributed Systems (IJCNDS)},
  year =          2009,
  month =         {June},
  volume =        2,
  number =        4,
  pages =         {375--400},
  publisher =     {Inderscience Publishers},
  copyright =     {Inderscience Enterprises},
  DOI =           {10.1504/IJCNDS.2009.026555},
  URL =           {https://www.cs.dartmouth.edu/~kotz/research/li-ijcnds/index.html},
  abstract =      {We consider a distributed system that disseminates high-volume event streams to many simultaneous monitoring applications over a low-bandwidth network. For bandwidth efficiency, we propose a collaborative data-reduction mechanism, ``group-aware stream filtering'', used together with multicast, to select a small set of necessary data that satisfy the needs of a group of subscribers simultaneously. We turn data-compressing filters into group-aware filters by exploiting two overlooked, yet important, properties of monitoring applications: 1) many of them 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. We provide a general framework that treats the group-aware stream filtering problem completely; we prove the problem NP-hard and thus provide a suite of heuristic algorithms that ensure data quality (specifically, granularity and timeliness) while collaboratively reducing data. The framework is extensible and supports a diverse range of filters. Our prototype-based evaluation shows that group-aware stream filtering is effective in trading CPU time for data reduction, compared with self-interested filtering.},
}