Document Type
Conference Proceeding
Publication Date
10-1-2008
Abstract
The inherent uncertainty of data present in numerous applications such as sensor databases, text annotations, and information retrieval motivate the need to handle imprecise data at the database level. Uncertainty can be at the attribute or tuple level and is present in both continuous and discrete data domains. This paper presents a model for handling arbitrary probabilistic uncertain data (both discrete and continuous) natively at the database level. Our approach leads to a natural and efflcient representation for probabilistic data. We develop a model that is consistent with possible worlds semantics and closed under basic relational operators. This is the first model that accurately and efficiently handles both continuous and discrete uncertainty. The model is implemented in a real database system (PostgreSQL) and the effectiveness and efficiency of our approach is validated experimentally. © 2008 IEEE.
Publication Source (Journal or Book title)
Proceedings - International Conference on Data Engineering
First Page
1053
Last Page
1061
Recommended Citation
Singh, S., Mayfield, C., Shah, R., Prabhakar, S., Hambrusch, S., Neville, J., & Cheng, R. (2008). Database support for probabilistic attributes and tuples. Proceedings - International Conference on Data Engineering, 1053-1061. https://doi.org/10.1109/ICDE.2008.4497514