Consequences of stratified sampling in graphics
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
NiagaraCQ: a scalable continuous query system for Internet databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Sampling algorithms: lower bounds and applications
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Exploitng event stream interpretation in publish-subscribe systems
Proceedings of the twentieth annual ACM symposium on Principles of distributed computing
Sampling from a moving window over streaming data
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Continuously adaptive continuous queries over streams
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Introduction to Algorithms
Adaptive filters for continuous queries over distributed data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Time and space optimization for processing groups of multi-dimensional scientific queries
Proceedings of the 18th annual international conference on Supercomputing
Sampling algorithms in a stream operator
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Adaptive stream filters for entity-based queries with non-value tolerance
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Group-aware stream filtering
| Hi-index | 0.00 |
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 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. 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 preserving bandwidth. Our evaluation shows that group-aware stream filtering is effective in trading CPU time for bandwidth savings, compared with self-interested filtering.