Dscaler

Abstract

The Dataset Scaling Problem (DSP) defined in previous work states: Given an empirical set of relational tables D and a scale factor s, generate a database state D that is similar to D but s times its size . A DSP solution is useful for application development ( s < 1), scalability testing ( s > 1) and anonymization ( s = 1). Current solutions assume all table sizes scale by the same ratio s . However, a real database tends to have tables that grow at different rates. This paper therefore considers non-uniform scaling (nuDSP), a DSP generalization where, instead of a single scale factor s , tables can scale by different factors. D scaler is the first solution for nuDSP. It follows previous work in achieving similarity by reproducing correlation among the primary and foreign keys. However, it introduces the concept of a correlation database that captures fine-grained, per-tuple correlation. Experiments with well-known real and synthetic datasets D show that D scaler produces D with greater similarity to D than state-of-the-art techniques. Here, similarity is measured by number of tuples, frequency distribution of foreign key references, and multi-join aggregate queries.