Estimating Sufficient Sample Sizes for Approximate Decision Support Queries
Sampling schemes for approximate processing of highly selective decision support queries need to retrieve sufficient number of records that can provide reliable results within acceptable error limits. The k-MDI tree is an innovative index structure that supports drawing rich samples of relevant reco...
| Main Authors: | , , |
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| Format: | Book Chapter |
| Published: |
Springer
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/13163 |
| _version_ | 1848748274196938752 |
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| author | Rudra, Amit Gopalan, Raj Achuthan, Narasimaha |
| author_facet | Rudra, Amit Gopalan, Raj Achuthan, Narasimaha |
| author_sort | Rudra, Amit |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Sampling schemes for approximate processing of highly selective decision support queries need to retrieve sufficient number of records that can provide reliable results within acceptable error limits. The k-MDI tree is an innovative index structure that supports drawing rich samples of relevant records for a given set of dimensional attribute ranges. This paper describes a method for estimating sufficient sample sizes for decision support queries based on inverse simple random sampling without replacement (SRSWOR). Combined with a k-MDI tree index, this method is shown to offer a reliable approach to approximate query processing for decision support. |
| first_indexed | 2025-11-14T07:02:26Z |
| format | Book Chapter |
| id | curtin-20.500.11937-13163 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:02:26Z |
| publishDate | 2014 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-131632017-09-13T14:57:34Z Estimating Sufficient Sample Sizes for Approximate Decision Support Queries Rudra, Amit Gopalan, Raj Achuthan, Narasimaha Sampling schemes for approximate processing of highly selective decision support queries need to retrieve sufficient number of records that can provide reliable results within acceptable error limits. The k-MDI tree is an innovative index structure that supports drawing rich samples of relevant records for a given set of dimensional attribute ranges. This paper describes a method for estimating sufficient sample sizes for decision support queries based on inverse simple random sampling without replacement (SRSWOR). Combined with a k-MDI tree index, this method is shown to offer a reliable approach to approximate query processing for decision support. 2014 Book Chapter http://hdl.handle.net/20.500.11937/13163 10.1007/978-3-319-09492-2_6 Springer restricted |
| spellingShingle | Rudra, Amit Gopalan, Raj Achuthan, Narasimaha Estimating Sufficient Sample Sizes for Approximate Decision Support Queries |
| title | Estimating Sufficient Sample Sizes for Approximate Decision Support Queries |
| title_full | Estimating Sufficient Sample Sizes for Approximate Decision Support Queries |
| title_fullStr | Estimating Sufficient Sample Sizes for Approximate Decision Support Queries |
| title_full_unstemmed | Estimating Sufficient Sample Sizes for Approximate Decision Support Queries |
| title_short | Estimating Sufficient Sample Sizes for Approximate Decision Support Queries |
| title_sort | estimating sufficient sample sizes for approximate decision support queries |
| url | http://hdl.handle.net/20.500.11937/13163 |