Estimating Sufficient Sample Sizes for Approximate Decision Support Queries
| dc.contributor.author | Rudra, Amit | |
| dc.contributor.author | Gopalan, Raj | |
| dc.contributor.author | Achuthan, Narasimaha | |
| dc.date.accessioned | 2017-01-30T11:35:17Z | |
| dc.date.available | 2017-01-30T11:35:17Z | |
| dc.date.created | 2015-05-11T20:00:41Z | |
| dc.date.issued | 2014 | |
| dc.identifier.citation | Rudra, A. and Gopalan, R. and Achuthan, N. 2014. Estimating Sufficient Sample Sizes for Approximate Decision Support Queries. In S. Hammoudi, J. Cordeiro, L.A. Maciaszek, J. Filipe (eds), Enterprise Information Systems, 85-99. Switzerland: Springer. | |
| dc.identifier.uri | http://hdl.handle.net/20.500.11937/13163 | |
| dc.identifier.doi | 10.1007/978-3-319-09492-2_6 | |
| dc.description.abstract |
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. | |
| dc.publisher | Springer | |
| dc.title | Estimating Sufficient Sample Sizes for Approximate Decision Support Queries | |
| dc.type | Book Chapter | |
| dcterms.source.startPage | 85 | |
| dcterms.source.endPage | 99 | |
| dcterms.source.title | Enterprise Information Systems | |
| dcterms.source.isbn | 9783319094915 | |
| dcterms.source.place | Switzerland | |
| dcterms.source.chapter | 31 | |
| curtin.department | School of Information Systems | |
| curtin.accessStatus | Fulltext not available |