The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
On optimizing an SQL-like nested query
Open accessAbstract
SQL is a high-level nonprocedural data language which has received wide recognition in relational databases. One of the most interesting features of SQL is the nesting of query blocks to an arbitrary depth. An SQL-like query nested to an arbitrary depth is shown to be composed of five basic types of nesting. Four of them have not been well understood and more work needs to be done to improve their execution efficiency. Algorithms are developed that transform queries involving these basic types of nesting into semantically equivalent queries that are amenable to efficient processing by existing query-processing subsystems. These algorithms are then combined into a coherent strategy for processing a general nested query of arbitrary complexity.
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Cites
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Cited by
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Snodgrass, R. (1987) · ACM Transactions on Database Systems · doi:10.1145/22952.22956 · Cited by 607
Survey on Chatbot Design Techniques in Speech Conversation Systems
A., S., & John, D. (2015) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2015.060712 · Cited by 479
Metadata
- Authors
- Kim, W.
- Venue
- ACM Transactions on Database Systems
- Published
- 1982-09
- Volume
- 7
- Issue
- 3
- Pages
- 443-469
- ISSN
- 0362-5915 (ISSN-L)
- 1557-4644
- OpenAlex
- W2008116177
- Cited by
- 393
- References
- 14
- FWCI
- 17.00
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
