Database Evolution, by Scientists, for Scientists: A Case Study

Database management systems have been used to great advantage for industry usage scenarios. As science becomes increasingly dependent on carefully organized and curated data to inform and drive new discoveries, the need for database management systems has grown significantly. The long standing "...

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Bibliographic Details
Published in2023 IEEE 19th International Conference on e-Science (e-Science) pp. 1 - 10
Main Authors Schuler, Robert E., Singla, Jitin, Vallat, Brinda, White, Kate L., Berman, Helen M., Kesselman, Carl
Format Conference Proceeding
LanguageEnglish
Published IEEE 09.10.2023
Subjects
Data models
Data visualization
Programming
relational database
Relational databases
Rendering (computer graphics)
schema evolution
scientific data management
software engineering
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Summary:Database management systems have been used to great advantage for industry usage scenarios. As science becomes increasingly dependent on carefully organized and curated data to inform and drive new discoveries, the need for database management systems has grown significantly. The long standing "20 questions" method was advocated in early studies of applying relational databases for science in order to elicit requirements for designing and developing information systems for scientific data. It has been observed, however, that database designs become outdated within months of usage leading to degradation in the quality of the database schema. In addition, there is limited evidence that scientists themselves have the tools and processes necessary to develop and maintain scientific databases without reliance on database administrators. Beyond learning to query databases, scientists need tools to create and evolve databases and guidance on how to apply those tools to develop information systems. In this paper, we present a simplified methodology for database evolution for scientists and a case study of database evolution by a scientist in the context of a research database for cell modeling. We include a detailed analysis of the activities and processes employed by the scientist during the schema evolution. Our results show that a scientist can successfully evolve a complex information system driven by new research requirements.
ISSN:2325-3703
DOI:10.1109/e-Science58273.2023.10254872