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A data stream language and system designed for power and extensibility
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Source Conference on Information and Knowledge Management archive
Proceedings of the 15th ACM international conference on Information and knowledge management table of contents
Arlington, Virginia, USA
SESSION: Data streams and sensor data table of contents
Pages: 337 - 346  
Year of Publication: 2006
ISBN:1-59593-433-2
Authors
Yijian Bai  UCLA
Hetal Thakkar  UCLA
Haixun Wang  IBM T.J. Watson R. C.
Chang Luo  UCLA
Carlo Zaniolo  UCLA
Sponsors
ACM: Association for Computing Machinery
SIGIR: ACM Special Interest Group on Information Retrieval
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 21,   Downloads (12 Months): 120,   Citation Count: 8
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ABSTRACT

By providing an integrated and optimized support for user-defined aggregates (UDAs), data stream management systems (DSMS) can achieve superior power and generality while preserving compatibility with current SQL standards. This is demonstrated by the Stream Mill system that, through is Expressive Stream Language (ESL), efficiently supports a wide range of applications - including very advanced ones such as data stream mining, streaming XML processing, time-series queries, and RFID event processing. ESL supports physical and logical windows (with optional slides and tumbles) on both built-in aggregates and UDAs, using a simple framework that applies uniformly to both aggregate functions written in an external procedural languages and those natively written in ESL. The constructs introduced in ESL extend the power and generality of DSMS, and are conducive to UDA-specific optimization and efficient execution as demonstrated by several experiments.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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CITED BY  8

Collaborative Colleagues:
Yijian Bai: colleagues
Hetal Thakkar: colleagues
Haixun Wang: colleagues
Chang Luo: colleagues
Carlo Zaniolo: colleagues