Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/30705
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dc.titleUsing Map-reduce to scale an empirical database
dc.contributor.authorSHEN ZHONG
dc.date.accessioned2012-02-29T18:00:52Z
dc.date.available2012-02-29T18:00:52Z
dc.date.issued2011-12-19
dc.identifier.citationSHEN ZHONG (2011-12-19). Using Map-reduce to scale an empirical database. ScholarBank@NUS Repository.
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/30705
dc.description.abstractDatasets are crucial for testing in both industrial and academic fields. However, getting a dataset which has a proper size and can reflect the real data properties is not easy. Different from normal domain-specific benchmarks, UpSizeR is a tool that takes an empirical dataset D and a scale factor s as input and generates a synthetic dataset which keeps the properties of the original dataset but s times its size. UpSizeR is implemented using Map-Reduce which guarantees it could efficiently handle large datasets . In order to reduce I/O operations, we optimize our UpSizeR implementation to make it more efficient. We run queries on both the synthetic and the original datasets and compare the results to evaluate the similarity of both datasets.
dc.language.isoen
dc.subjectMap-Reduce,UpSizeR,Scale,Dataset,Database,Hadoop
dc.typeThesis
dc.contributor.departmentCOMPUTER SCIENCE
dc.contributor.supervisorTAY YONG CHIANG
dc.description.degreeMaster's
dc.description.degreeconferredMASTER OF SCIENCE
dc.identifier.isiutNOT_IN_WOS
Appears in Collections:Master's Theses (Open)

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