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

@InProceedings{li:quality,
  author =        {Ming Li and David Kotz},
  title =         {{Event Dissemination via Group-aware Stream Filtering}},
  booktitle =     {{Proceedings of the International Conference on Distributed Event-Based Systems (DEBS)}},
  year =          2008,
  month =         {July},
  pages =         {59--70},
  publisher =     {ACM},
  copyright =     {ACM},
  DOI =           {10.1145/1385989.1385998},
  URL =           {https://kotz.cs.dartmouth.edu/research/li-quality/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 \emph{group-aware stream filtering} approach, used together with multicasting, that exploits 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. Here we provide a general framework for the group-aware stream filtering problem, which we prove is NP-hard. We introduce a suite of heuristics-based algorithms that ensure data quality (specifically, granularity and timeliness) while preserving bandwidth. Our evaluation shows that group-aware stream filtering is effective in trading CPU time for bandwidth savings, compared with self-interested filtering.},
}