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| Kian-Lee Tan, Cheng Hian Goh, Beng Chin Ooi, "Query Rewriting for SWIFT (First) Answers," IEEE Transactions on Knowledge and Data Engineering, vol. 12, no. 5, pp. 694-714, September/October, 2000. | |||
| BibTex | x | ||
| @article{ 10.1109/69.877503, author = {Kian-Lee Tan and Cheng Hian Goh and Beng Chin Ooi}, title = {Query Rewriting for SWIFT (First) Answers}, journal ={IEEE Transactions on Knowledge and Data Engineering}, volume = {12}, number = {5}, issn = {1041-4347}, year = {2000}, pages = {694-714}, doi = {http://doi.ieeecomputersociety.org/10.1109/69.877503}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - JOUR JO - IEEE Transactions on Knowledge and Data Engineering TI - Query Rewriting for SWIFT (First) Answers IS - 5 SN - 1041-4347 SP694 EP714 EPD - 694-714 A1 - Kian-Lee Tan, A1 - Cheng Hian Goh, A1 - Beng Chin Ooi, PY - 2000 KW - WWW KW - Internet KW - progressive query evaluation KW - query optimization KW - query rewrite. VL - 12 JA - IEEE Transactions on Knowledge and Data Engineering ER - | |||
Abstract—Traditionally, the answer to a database query is construed as the set of all tuples that meet the criteria stated. Strict adherence to this notion in query evaluation is, however, increasingly unsatisfactory because decision makers are more prone to adopting an exploratory strategy for information search which we call “getting some answers quickly, and perhaps more later.” From a decision-maker's perspective, such a strategy is optimal for coping with information overload and makes economic sense (when used in conjunction with a micropayment mechanism). These new requirements present new opportunities for database query optimization. In this paper, we propose a progressive query processing strategy that exploits this behavior to conserve system resources and to minimize query response time and user waiting time. This is accomplished by the heuristic decomposition of user queries into subqueries that can be evaluated on demand. To illustrate the practicality of the proposed methods, we describe the architecture of a prototype system that provides a nonintrusive implementation of our approach. Finally, we present experimental results obtained from an empirical study conducted using an Oracle Server that demonstrate the benefits of the progressive query processing strategy.
[1] K. Koffka, Principles of Gestalt Psychology. New York: Harcourt-Brace, 1935. C. Ahlberg, and B. Shneiderman, “Visual Information Seeking: Tight Coupling of Dynamic Query Filters with Starfield Displays,” Proc. Conf. Human Factors and Computing Systems (CHI '94), pp. 313-317, 479-480, 1994.
[2] C. Ahlberg, C. Williamson, and B. Shneiderman, “Dynamic Queries for Information Exploration: An Implementation and Evaluation,” Proc. ACM CHI Int'l Conf. Human Factors in Computing, pp. 619-626, 1992.
[3] Oracle Corporation, Oracle7 Server—Application Developer's Guide, 1992.
[4] R. Bayardo and D. Miranker, “Processing Queries for the First Few Answers,” Proc. Third Int'l Conf. Information and Knowledge Management (CIKM '94), 1996.
[5] M. Carey and D. Kossmann, “On Saying Enough Already in SQL,” Proc. 1997 ACM-SIGMOD Int'l Conf. Management of Data, pp. 219–230, June 1997.
[6] M. Carey and D. Kossmann, “Reducing the Braking Distance of an SQL Query Engine,” Proc. 24th Int'l Conf. Very Large Data Bases, Aug. 1998.
[7] T. CatarciI. and I. Cruz, eds., SIGMOD RECORD: Special Issue on Information Visualization, vol. 4,p. 25, Dec. 1996.
[8] S. Chaudhuri and L. Gravano, “Evaluating Top-k Selection Queries,” Proc. Very Large Data Bases Conf., pp. 397-410, 1999.
[9] P.M. Hallam-Baker, “Micro Payment Transfer Protocol (MPTP version 0.1.),” W3C Working Draft inhttp://wwww.w3.org/pub/WWW/TRWD-mptp-951122 .
[10] J.M. Hellerstein, P.J. Haas, and H.J. Wang, "Online Aggregation," Proc. ACM SIGMOD Int'l Conf. Management of Data, ACM Press, New York, 1997, pp. 171-182.
[11] IBM Corporation, “DB2 Application Programming Guide for Common Servers, Version 2,” 1995.
[12] W. Kim, “On Optimizing an SQL-like Nested Query,” ACM Trans. Data Systems, Sept. 1982.
[13] F. Olken, “Random Sampling From Databases,” PhD thesis, University of California, Berkeley, 1993.
[14] R. Ramakrishnan and J. Gehrke, Database Management Systems, second ed., McGraw-Hill, 1999.
[15] K. Tan, C. Goh, and B. Ooi, “On Getting Some Answers Quickly, and Perhaps More Later,” Proc. ICDE '99 Conf., pp. 32–39, 1999.
[16] K. Tan, C. Goh, and B. Ooi, “Online Feedback for Nested Aggregate Queries with Multithreading,” Proc. 25th Int'l Conf. Very Large Data Bases (VLDB '99), pp. 18–29, 1999.
[17] A. Wilshut and P. Apers, “Dataflow Query Execution in a Parallel Main Memory System,” Proc. First Int'l Conf. Parallel and Distributed Information Systems, Dec. 1991.

