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A native extension of SQL for mining data streams

Published: 14 June 2005 Publication History

Abstract

ESL1 enables users to develop stream applications in an SQL-like high level language that provides the ease-of-use of a declarative language, which is Turing complete in terms of expressive power [11].

References

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The Stream Mill Project. http://wis.cs.ucla.edu/stream-mill/
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Brian Babcock, Shivnath Babu, Rajeev Motwani, Jennifer Widom. Models and Issues in Data Streams, PODS, 2002.
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J. Chen, D. J. DeWitt, F. Tian, and Y. Wang. NiagaraCQ: A scalable continuous query system for internet databases. SIGMOD, pages 379--390, 2000.
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P. Domingos and G. Hulten. Mining high-speed datastreams. SIGKDD, pages 71--80, 2000.
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íSO/IEC JTC1/SC21 N10489, ISO//IEC 9075, "Committee Draft (CD), Database Language SQL", July 1996.
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Haixun Wang and Carlo Zaniolo. Using SQL to Build New Aggregates and Extenders for Object-Relational Systems. VLDB, 2000.
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Haixun Wang, Carlo Zaniolo. Extending SQL for Decision Support Applications. DMDW, 2002.
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Haixun Wang, Carlo Zaniolo. ESL: A Native Extension of SQL for Data Mining and Stream Computations UCLA CS Dept, Technical Report
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Haixun Wang, Carlo Zaniolo. ATLaS: A Native Extension of SQL for Data Mining. SIAM DM, 2003.
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Sam Madden, Mehul A. Shah, Joseph M. Hellerstein, Vijayshankar Raman. Continuously Adaptive Continuous Queries over Streams. SIGMOD, pages 49--61, 2002.
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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.
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Haixun Wang, Wei Fan, Philip S. Yu, and Jiawei Han. Mining Concept-Drifting Data Streams using Ensemble Classifiers. SIGKDD, 2003.
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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.
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WEB Information System Laboratory UCLA. An Introduction to the Expressive Stream Language (ESL). http://wis.cs.ucla.edu.
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Yun Chi, Haixun Wang, Philip Yu, and Richard Muntz. Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window. ICDM, 2004.
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S. Sarawagi, S. Thomas, R. Agrawal. Integrating Association Rule Mining with Relational Database Systems: Alternatives and Implications. SIGMOD, 1998.
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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:
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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.

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    cover image ACM Conferences
    SIGMOD '05: Proceedings of the 2005 ACM SIGMOD international conference on Management of data
    June 2005
    990 pages
    ISBN:1595930604
    DOI:10.1145/1066157
    • Conference Chair:
    • Fatma Ozcan
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 14 June 2005

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