The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Continual queries for Internet scale event-driven information delivery
Abstract
We introduce the concept of continual queries, describe the design of a distributed event-driven continual query system-OpenCQ, and outline the initial implementation of OpenCQ on top of the distributed interoperable information mediation system DIOM. Continual queries are standing queries that monitor update of interest and return results whenever the update reaches specified thresholds. In OpenCQ, users may specify to the system the information they would like to monitor (such as the events or the update thresholds they are interested in). Whenever the information of interest becomes available, the system immediately delivers it to the relevant users; otherwise, the system continually monitors the arrival of the desired information and pushes it to the relevant users as it meets the specified update thresholds. In contrast to conventional pull-based data management systems such as DBMSs and Web search engines, OpenCQ exhibits two important features: it provides push-enabled, event-driven, content-sensitive information delivery capabilities; and it combines pull and push services in a unified framework. By event-driven we mean that the update events of interest to be monitored are specified by users or applications. By content-sensitive, we mean the evaluation of the trigger condition happens only when a potentially interesting change occurs. By push-enabled, we mean the active delivery of query results or triggering of actions without user intervention.
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Cites
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Cited by
TinyDB: an acquisitional query processing system for sensor networks
Madden, S. R., Franklin, M. J., Hellerstein, J. M., & Hong, W. (2005) · ACM Transactions on Database Systems · doi:10.1145/1061318.1061322 · Cited by 1,899
The design of an acquisitional query processor for sensor networks
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Processing flows of information: From data stream to complex event processing
Cugola, G., & Margara, A. (2012) · ACM Computing Surveys · doi:10.1145/2187671.2187677 · Cited by 833
NiagaraCQ: a scalable continuous query system for Internet databases
Chen, J., DeWitt, D. J., Tian, F., & Wang, Y. (2000) · Proceedings of the 2000 ACM SIGMOD international conference on Management of data · doi:10.1145/342009.335432 · Cited by 481
Adaptive filters for continuous queries over distributed data streams
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Metadata
- Authors
- Liu, L., Pu, C., & Tang, W.
- Venue
- IEEE Transactions on Knowledge and Data Engineering
- Published
- 1999
- Volume
- 11
- Issue
- 4
- Pages
- 610-628
- ISSN
- 1041-4347 (ISSN-L)
- OpenAlex
- W2097432384
- Cited by
- 332
- References
- 19
- FWCI
- 36.54
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
