SYNTHETICALLY SCALING AN EMPIRICAL DATASET

Abstract

Large-scale enterprises, like Amazon and Douban, have enormous datasets. For research and development, it is impractical to run experiments with such a large dataset. It is therefore often necessary to obtain a smaller version of the dataset for experiments. We call this the scaling down problem.

At the other extreme, a start-up company may have a small dataset, but wants to test the scalability of their system. They may, therefore, want to have a larger (and necessarily) synthetic version of their current empirical dataset. We call this the scaling up problem.

This motivates the Dataset Scaling Problem (DSP): Given an original dataset D and a scale factor s, generate a scaled dataset D' that is similar to D but s times its size.

This thesis studies DSP in the domain of graph and relational databases.