On-the-fly sharing for streamed aggregation

Proceedings of the ACM SIGMOD International Conference on Management of Data

Volumn , Issue , 2006, Pages 623-634

  Krishnamurthy, Sailesh ^a   Wu, Chung ^b   Franklin, Michael ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b GOOGLE INC   (United States)

Author keywords

Aggregation; Multiple query optimization; Shared processing; Streaming data

Indexed keywords

MULTIPLE-QUERY OPTIMIZATION; NETWORK INTRUSION DETECTION; STREAMING DATA;

COMPUTER MONITORS; OPTIMIZATION; QUERY PROCESSING; SCALABILITY;

DATABASE SYSTEMS;

EID: 34250685048     PISSN: 07308078     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1142473.1142543     Document Type: Conference Paper

References (31)

1. A. Arasu et al., Resource sharing in continuous sliding-window aggregates. In VLDB. 2004.
2. A. Arasu, et al., The CQL continuous query language: Semantic foundations and query execution. VLDB Journal, (To appear).
3. F. Bancilhon, et al., FAD, a powerful and simple database language. In VLDB. 1987.
4. D. Carney, et al., Monitoring streams - a new class of data management applications. In VLDB. 2002.
5. S. Chandrasekaran et al., Streaming queries over streaming data. In VLDB. 2002.
6. S. Chandrasekaran, et al., TelegraphCQ: Continuous dataflow processing for an uncertain world. In CIDR. 2003.
7. J. Chen, et al., NiagaraCQ: a scalable continuous query system for Internet databases. In SIGMOD. 2000.
8. C. D. Cranor, et al., Gigascope: A stream database for network applications. In SIGMOD. 2003.
9. M. Denny et al., Predicate result range caching for continuous queries. In SIGMOD. 2005.
10. P. M. Deshpande, et al. Caching multidimensional queries using chunks. In SIGMOD. 1998.
11. C. L. Forgy. Rete: A fast algorithm for the many pattern/many object match problem. Artifical Intelligence, 19(1): 17-37, September 1982.
12. M. J. Franklin, et al., Design considerations for high fan-in systems: The HiFi approach. In CIDR. 2005.
13. L. Golab et al., Update-pattern-aware modeling and processing of continuous queries. In SIGMOD. 2005.
14. G. Graefe. Query evaluation techniques for large databases. ACM Computing Surveys, 25(2):73-170, June 1993.
15. J. Gray, et al., Data Cube: a relational aggregation operator generalizing group-by, cross-tab and sub-total. In ICDE. February 1996.
16. M. A. Hammad, et al., Efficient pipelined execution of sliding window queries over data streams. Technical Report CSD TR#03-035, Purdue, 2003.
17. M. A. Hammad, et al., Scheduling for shared window joins over data streams. In vldb. 2003.
18. V. Harinarayan, et al., Implementing data cubes efficiently. In SIGMOD. 1996.
19. M. Jarke. Common subexpression isolation in multiple query optimization. In Query Processing in Database Systems. Springer Verlag, 1985.
20. S. Krishnamurthy, et al., TelegraphCQ: An architectural status report. IEEE DE. Bull., 26(1), 2003.
21. S. Krishnamurthy, et al., The case for precision sharing. In VLDB. 2004.
22. J. Li, et al., No pane, no gain: Efficient evaluation of sliding-window aggregates over data streams. SIGMOD Record, March 2005.
23. S. R. Madden, et al., Continuously adaptive continuous queries over streams. In SIGMOD. 2002.
24. S. R. Madden, et al., TAG: a tiny aggregation service for ad-hoc sensor networks. In OSDI. 2002.
25. R. Motwani, et al., Query processing, resource management, and approximation in a data stream management system. In CIDR. 2003.
26. NASDAQ. NASTRAQ: North American Securities Tracking and Quantifying System, http://www.nastraq.com/description.htm.
27. NYSE. NYSE TAQ: Daily Trades and Quotes Database. http://www.nysedata. com/info/productdetail.asp?dpbid=13.
28. P. Roy, et al., Efficient and extensible algorithms for multi query optimization. In SIGMOD. 2000.
29. T. K. Sellis. Multiple-query optimization. ACM TODS, March 1988.
30. D. Srivastava, et al., Multiple aggregations over data streams. In SIGMOD. 2005.
31. N. Tatbul, et al., Load shedding in a data stream manager. In VLDB. 2003.