SQL extension for exploring multiple tables

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SQL extension for exploring multiple tables

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Conference on Information and Knowledge Management archive
Proceeding of the 17th ACM conference on Information and knowledge management table of contents

Napa Valley, California, USA

POSTER SESSION: Poster session 1 database table of contents

Pages 1331-1332

Year of Publication: 2008

ISBN:978-1-59593-991-3

Authors

Sung Jin Kim	University of California, Los Angeles (UCLA), Los Angeles, CA, USA
Junghoo John Cho	University of California, Los Angeles (UCLA), Los Angeles, CA, USA

Sponsors

ACM: Association for Computing Machinery
SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web
SIGIR: ACM Special Interest Group on Information Retrieval

Publisher

ACM New York, NY, USA

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ABSTRACT

The standard SQL assumes that the users are aware of all tables and their schemas to write queries. This assumption may be valid when the users deal with a relatively small number of tables, but writing a SQL query on a large number of tables is often challenging; (1) the users do not know what tables are relevant to their query, (2) it is too cumbersome to explicitly list tens of (or even hundreds of) relevant tables in the FROM clause and (3)

the schemas of those tables are not identical. We now propose an intuitive yet powerful extension to SQL that helps users explore and aggregate information spread over a large number of tables.

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.

	1	S. Madden, M. J. Franklin, J. M. Hellerstein, and W. Hong. TinyDB: an Acquisitional Query Processing System for Sensor Networks. In Proceedings of the International Conference on Management of Data (SIGMOD), pages 778--787, 2003.
	2	Philippe Bonnet , Johannes Gehrke , Praveen Seshadri, Towards Sensor Database Systems, Proceedings of the Second International Conference on Mobile Data Management, p.3-14, January 08-10, 2001
	3	Erhard Rahm , Philip A. Bernstein, A survey of approaches to automatic schema matching, The VLDB Journal — The International Journal on Very Large Data Bases, v.10 n.4, p.334-350, December 2001 [doi>10.1007/s007780100057]
	4	S. Reddy, G. Chen, B. Fulkerson, S. J. Kim, U. Park, N. Yau, J. Cho, M. Hansen, and J. Heidemann. Sensor-Internet Share and Search: Enabling Collaboration of Citizen Scientists. In IPSN (SDI), pages 1--12, 2007.