A High-Level User-Oriented Framework for Database Evolution. Schuler, R. E. & Kessleman, C. In Proceedings of the 31st International Conference on Scientific and Statistical Database Management, of SSDBM '19, pages 157–168, New York, NY, USA, 2019. Association for Computing Machinery.
doi  abstract   bibtex   
Databases are well suited to the task of describing and organizing research datasets, however, the difficulties of using database management systems effectively have resulted in their limited usage among domain scientists. Scientists operate in an environment that is changing steadily with new experimental protocols, instruments, and discoveries that impact what datasets they generate and how they describe and organize them. In order to manage datasets for a scientific application, scientists need to routinely revise their database schemas to reflect these changes. Unfortunately, evolving a database is one of the well-known and most difficult aspects of database usage. The conventional data definition and manipulation languages offer relatively low-level programming abstractions to perform complex database evolution tasks, and therefore require specialized technical skills not possessed by most domain scientists. A simplified means of expressing database evolution operations can reduce the effort of keeping the scientific database in sync with changing requirements. This paper presents a high-level, user-oriented, schema evolution framework with an algebra of specialized schema modification operators. The approach allows introduction of novel operators as motivated by new requirements and is amenable to well established optimization techniques for efficient planning and execution. We present the framework and its implementation, and we demonstrate its utility in an exemplar use case and performance evaluation.
@inproceedings{Schuler2019,
	abstract = {Databases are well suited to the task of describing and organizing research datasets, however, the difficulties of using database management systems effectively have resulted in their limited usage among domain scientists. Scientists operate in an environment that is changing steadily with new experimental protocols, instruments, and discoveries that impact what datasets they generate and how they describe and organize them. In order to manage datasets for a scientific application, scientists need to routinely revise their database schemas to reflect these changes. Unfortunately, evolving a database is one of the well-known and most difficult aspects of database usage. The conventional data definition and manipulation languages offer relatively low-level programming abstractions to perform complex database evolution tasks, and therefore require specialized technical skills not possessed by most domain scientists. A simplified means of expressing database evolution operations can reduce the effort of keeping the scientific database in sync with changing requirements. This paper presents a high-level, user-oriented, schema evolution framework with an algebra of specialized schema modification operators. The approach allows introduction of novel operators as motivated by new requirements and is amenable to well established optimization techniques for efficient planning and execution. We present the framework and its implementation, and we demonstrate its utility in an exemplar use case and performance evaluation.},
	address = {{New York, NY, USA}},
	author = {Schuler, Robert E. and Kessleman, Carl},
	booktitle = {Proceedings of the 31st {{International Conference}} on {{Scientific}} and {{Statistical Database Management}}},
	doi = {10.1145/3335783.3335787},
	file = {/Users/schuler/Zotero/storage/T8CSAR3I/Schuler and Kesselman - 2019 - A High-level User-oriented Framework for Database .pdf},
	isbn = {978-1-4503-6216-0},
	keywords = {Database evolution,Schema evolution,Scientific databases},
	pages = {157--168},
	publisher = {{Association for Computing Machinery}},
	series = {{{SSDBM}} '19},
	title = {A {{High-Level User-Oriented Framework}} for {{Database Evolution}}},
	year = {2019},
	bdsk-url-1 = {https://doi.org/10.1145/3335783.3335787}}

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