Databases today are carefully engineered: there is an expensive and deliberate design process, after which a database schema is defined; during this design process, various possible instance examples and use cases are hypothesized and carefully analyzed; finally, the schema is ready and then can be populated with data. All of this effort is a major barrier to database adoption. In this paper, we explore the possibility of organic database creation instead of the traditional engineered approach. The idea is to let the user start storing data in a database with a schema that is just enough to cove the instances at hand. We then support efficient schema evolution as new data instances arrive. By designing the database to evolve, we can sidestep the expensive front-end cost of carefully engineering the design of the database. The same set of issues also apply to database querying. Today, databases expect queries to be carefully specified, and to be valid with respect to the database schema. In contrast, the organic query specification model would allow users to construct queries incrementally, with little knowledge of the database. We also examine this problem in this paper. © 2011 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Jagadish, H. V., Nandi, A., & Qian, L. (2011). Organic databases. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7108 LNCS, pp. 49–63). https://doi.org/10.1007/978-3-642-25731-5_5
Mendeley helps you to discover research relevant for your work.