Enhanced Regular Expression as a DGL for Generation of Synthetic Big Data

Enhanced Regular Expression as a DGL for Generation of Synthetic Big Data

Big data

Probability distributions

Data privacy

Petroleum reservoir evaluation

Data Analytics

Pattern matching

Query languages

Synthetic data generation is generally used in performance evaluation and function tests in data-intensive applications, as well as in various areas of data analytics, such as privacy-preserving data publishing (PPDP) and statistical disclosure limit/control. A significant amount of research has been conducted on tools and languages for data generation. However, existing tools and languages have been developed for specific purposes and are unsuitable for other domains. In this article, we propose a regular expression-based data generation language (DGL) for flexible big data generation. To achieve a general-purpose and powerful DGL, we enhanced the standard regular expressions to support the data domain, type/format inference, sequence and random generation, probability distributions, and resource reference. To efficiently implement the proposed language, we propose caching techniques for both the intermediate and database queries. We evaluated the proposed improvement experimentally. 2023 KIPS

1-16

1

19

Cheng, Kai

Abe, Keisuke

Journal of Information Processing Systems

2023

journalArticle

1976913X

10.3745/JIPS.04.0262