A native extension of SQL for mining data streams

Proceedings of the ACM SIGMOD International Conference on Management of Data

Volumn , Issue , 2005, Pages 873-875

  Luo, Chang ^a   Thakkar, Hetal ^a   Wang, Haixun ^b   Zaniolo, Carlo ^a  

^a University of California   (United States)

^b IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 29844440052     PISSN: 07308078     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1066157.1066271     Document Type: Conference Paper

References (21)

1. The Stream Mill Project. http://wis.cs.ucla.edu/stream-mill/
2. Brian Babcock, Shivnath Babu, Rajeev Motwani, Jennifer Widom. Models and Issues in Data Streams, PODS, 2002.
3. J. Chen, D. J. DeWitt, F. Tian, and Y. Wang. NiagaraCQ: A scalable continuous query system for internet databases. SIGMOD, pages 379-390, 2000.
4. P. Domingos and G. Hulten. Mining high-speed datastreams. SIGKDD, pages 71-80, 2000.
5. ISO/IEC JTC1/SC21 N10489, ISO//IEC 9075, "Committee Draft (CD), Database Language SQL", July 1996.
6. Haixun Wang and Carlo Zaniolo. Using SQL to Build New Aggregates and Extenders for Object-Relational Systems. VLDB, 2000.
7. Haixun Wang, Carlo Zaniolo. Extending SQL for Decision Support Applications. DMDW, 2002.
8. Haixun Wang, Carlo Zaniolo. ESL: A Native Extension of SQL for Data Mining and Stream Computations UCLA CS Dept, Technical Report
9. Haixun Wang, Carlo Zaniolo. ATLaS: A Native Extension of SQL for Data Mining. SIAM DM, 2003.
10. Sam Madden, Mehul A. Shah, Joseph M. Hellerstein, Vijayshankar Raman. Continuously Adaptive Continuous Queries over Streams. SIGMOD, pages 49-61, 2002.
11. Yan-Nei Law, Haixun Wang, Carlo Zaniolo. Query Languages and Data Models for Database Sequences and Data Streams. VLDB, 2004.
12. Mayur Datar, Aristides Gionis, Piotr Indyk, and Rajeev Motwani. Maintaining stream statistics over sliding windows: (extended abstract). In SODA, 2002.
13. Haixun Wang, Wei Fan, Philip S. Yu, and Jiawei Han. Mining Concept-Drifting Data Streams using Ensemble Classifiers. SIGKDD, 2003.
14. D. Carney, U. Cetintemel, M. Cherniack, C. Convey, S. Lee, G. Seidman, M. Stonebraker, N. Tatbul, and S. Zdonik. Monitoring streams - a new class of data management applications. VLDB 2002.
15. WEB Information System Laboratory UCLA. An Introduction to the Expressive Stream Language (ESL). http://wis.cs.ucla.edu.
16. Yun Chi, Haixun Wang, Philip Yu, and Richard Muntz. Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window. ICDM, 2004.
17. S. Sarawagi, S. Thomas, R. Agrawal. Integrating Association Rule Mining with Relational Database Systems: Alternatives and Implications. SIGMOD, 1998.
18. Lukasz Golab and M. Tamer -zsu. Issues in data stream management. ACM SIGMOD Record, 32(2):5-14, 2003.
19. B. Babcock, S. Babu, M. Datar, R. Motawani, and J. Widom. Models and issues in data stream systems. PODS, 2002.
20. Fang Chu and Carlo Zaniolo. Fast and Light Boosting for Adaptive Mining of Data Streams. PAKDD 2004:
21. A. Arasu, S. Babu, and J. Widom. An abstract semantics and concrete language for continuous queries over streams and relations. Technical report, Stanford University, 2002.