@inproceedings{li:quality,
    author = {Ming Li and David Kotz},
    title = {Quality-managed Group-aware Stream Filtering},
    booktitle = {Proceedings of the Second International Conference on
Distributed Event-Based Systems (DEBS)},
    year = {2008},
    month = {July},
    pages = {59--70},
    publisher = {ACM Press},
    copyright = {ACM},
    later = {li:ijcnds},
    doi = {10.1145/1385989.1385998},
    url = {http://www.cs.dartmouth.edu/~dfk/papers/li-quality.pdf},
    keyword = {wireless network, data dissemination, overlay multicasting, data
filtering, bandwidth reduction},
    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
\textit{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.}
}