"UpSizeR: Synthetically Scaling an Empirical Relational Database"
| dc.contributor.author | TAY, Y. C. | en_US |
| dc.contributor.author | DAI, Bingtian | en_US |
| dc.contributor.author | WANG, Tao | en_US |
| dc.contributor.author | SUN, Yang | en_US |
| dc.contributor.author | LIN, Yong | en_US |
| dc.contributor.author | LIN, Yuting | en_US |
| dc.date.accessioned | 2010-12-27T09:50:40Z | en_US |
| dc.date.accessioned | 2017-01-23T07:00:16Z | |
| dc.date.available | 2010-12-27T09:50:40Z | en_US |
| dc.date.available | 2017-01-23T07:00:16Z | |
| dc.date.issued | 2010-12-17 | en_US |
| dc.description.abstract | This paper presents UpSizeR, a software that takes as input an empirical relational dataset D and a scale factor s, and generates a synthetic dataset e D' that is similar to D but s times its size. Such a tool can be useful for scaling up D for scalability testing (s > 1), scaling down for application debugging (s < 1), or anonymization (s = 1). Experiments with Flickr show that query results and response times on UpSizeR output match those on crawled data. They also accurately predict throughput degradation for a scale out test. | en_US |
| dc.format.extent | 216607 bytes | en_US |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.uri | https://dl.comp.nus.edu.sg/xmlui/handle/1900.100/3342 | en_US |
| dc.language.iso | en_US | en_US |
| dc.relation.ispartofseries | TR12/10 | en_US |
| dc.title | "UpSizeR: Synthetically Scaling an Empirical Relational Database" | en_US |
| dc.type | Technical Report | en_US |