A Machine-Readable Framework for Open Government Data and Government Data Analytics
DOI:
https://doi.org/10.14456/jiskku.2021.9Keywords:
Machine-Readable, Machine Readability, Open Government Data, Government Data AnalyticsAbstract
Purpose of this study: This documentary research aimed at (1) examining research papers and other related materials on machine-readable and conducting their content analysis (2) developing machine-readable framework for transforming data into machine-readable data for computer processing and making it open to the public and (3) locating machine-readable format in the public open dataset of distinguished open data portal.
Methodology: Research papers and related works, including data guidelines were examined and analyzed. Then the machine-readable framework and characteristics of government open data were set. Moreover, the proposed framework was used to evaluate the machine-readable format in the public open dataset available on Asia Open Data Portal (AODP).
Main findings: There were 6 characteristics of machine-readable dataset consisting of (1) data format which could be automatically processed by a computer, and being structured data (2) data format which no entity had exclusive control and must be encrypted internationally format (3) data format that could be processed while ensuring no semantic meaning was lost (4) consistency of data in format and type (5) variable naming that follows the rules and naming conventions and (6) being the data which could be interrogated and processed by computer code and classified in 4 levels: basic, medium, advanced and optional. In addition, the evaluation of the dataset revealed that most datasets were opened at a medium level. Taiwan dataset was found to be the highest open data while Thailand opened published data at the basic and medium levels.
Applications of this study: Findings from this investigation can be used to develop guidelines for data preparation and open data policy formulation. Also, the study is useful in data management which is a good preparation for future government big data analytics.
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