DBScholar

Back to papers

Query-Aware Partitioning for Monitoring Massive Network Data Streams

Summary: Proposes query-aware partitioning for distributed DSMSs to beat conventional methods. Analyzes query sets to pick optimal partitioning and reconcile cross-query requirements; experiments on high-rate traffic show improved load distribution. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4122
Venue
SIGMOD
Year
2008
Pagerank
5.6904549e-05
Overall Rank
7,146 | 50.98%
DOI
10.1145/1376616.1376730

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{johnson_sigmod08,
        title = {{Query-Aware Partitioning for Monitoring Massive Network Data Streams}},
        author = {Johnson, Theodore and Muthukrishnan, S. and Shkapenyuk, Vladislav and Spatscheck, Oliver},
        series = {{SIGMOD} '08},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/1376616.1376730},
        url = {https://dl.acm.org/doi/10.1145/1376616.1376730},
        year = {2008}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
1,915 S-Store: Streaming Meets Transaction Processing 2015 VLDB 9.4884706e-05
4,918 Massive Scale-out of Expensive Continuous Queries 2011 VLDB 6.4469122e-05
Previous Page 1 / 1 Next

Outgoing Citations (Sorted by Pagerank)

Showing 10 of 10 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Previous Page 1 / 1 Next

Semantically Similar Papers