A possibility-theory-based model for relational databases containing uncertain attribute values

Li Li

doi:10.1117/12.2013804

13 March 2013 A possibility-theory-based model for relational databases containing uncertain attribute values

Li Li

Author Affiliations +

Proceedings Volume 8784, Fifth International Conference on Machine Vision (ICMV 2012): Algorithms, Pattern Recognition, and Basic Technologies; 87840J (2013) https://doi.org/10.1117/12.2013804
Event: Fifth International Conference on Machine Vision (ICMV 12), 2012, Wuhan, China

Abstract

In this paper, we have presented an uncertain database model based on possibility certainty. The idea is to associate every candidate value (or disjunction of such values) representing an ill-known piece of data with a degree expressing the extent to which the candidate value (or disjunction) is certain. We have extended relational algebra in this context and shown that the model constitutes a strong representation system for this set of operators. The only constraints concerns (i) the join which has to be based on an equality condition, (ii) the union, Cartesian product, and join operations which must take independent relations as arguments. An important result is that the data complexity of these operations is the same as in the classical database case.

Citation Download Citation

Li Li "A possibility-theory-based model for relational databases containing uncertain attribute values ", Proc. SPIE 8784, Fifth International Conference on Machine Vision (ICMV 2012): Algorithms, Pattern Recognition, and Basic Technologies, 87840J (13 March 2013); https://doi.org/10.1117/12.2013804

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