Journal of Information Processing Systems

  • 제19권1호
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  • Pages.1-16
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  • 2023
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  • 1976-913X(pISSN)
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  • 2092-805X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
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Enhanced Regular Expression as a DGL for Generation of Synthetic Big Data

  • Kai, Cheng (Dept. of Information Science, Kyushu Sangyo University) ;
  • Keisuke, Abe (Dept. of Information Science, Kyushu Sangyo University)
  • 투고 : 2021.01.15
  • 심사 : 2021.12.26
  • 발행 : 2023.02.28
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초록

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.

키워드

과제정보

This study was supported by a grant from the JSPS Grants-in-Aid for Scientific Research (No. 20K11836) to Kai Cheng.

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