UpSizeR: Synthetically scaling an empirical relational database

Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.is.2013.07.004

Title:	UpSizeR: Synthetically scaling an empirical relational database
Authors:	Tay, Y.C. Dai, B.T. Wang, D.T. Sun, E.Y. Lin, Y. Lin, Y.
Keywords:	Application-specific benchmarking Attribute value correlation Empirical dataset Scale factor Social networks Synthetic data generation
Issue Date:	2013
Citation:	Tay, Y.C., Dai, B.T., Wang, D.T., Sun, E.Y., Lin, Y., Lin, Y. (2013). UpSizeR: Synthetically scaling an empirical relational database. Information Systems 38 (8) : 1168-1183. ScholarBank@NUS Repository. https://doi.org/10.1016/j.is.2013.07.004
Abstract:	The TPC benchmarks have helped users evaluate database system performance at different scales. Although each benchmark is domain-specific, it is not equally relevant to different applications in the same domain. The present proliferation of applications also leaves many of them uncovered by the very limited number of current TPC benchmarks. There is therefore a need to develop tools for application-specific database benchmarking. This paper presents UpSizeR, a software that addresses the Dataset Scaling Problem: Given an empirical set of relational tables D and a scale factor s, generate a database state DËœ 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 testing (s
Source Title:	Information Systems
URI:	http://scholarbank.nus.edu.sg/handle/10635/104444
ISSN:	03064379
DOI:	10.1016/j.is.2013.07.004
Appears in Collections:	Staff Publications

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