Abstract
Many prevail applications, such as data cleaning, sensor networks, tracking moving objects, emerge an increasing demand for managing uncertain data. Probabilistic relational databases support uncertain data management. Informally, a probabilistic database is a probability distribution over a set of deterministic databases (namely, possible worlds). Assumption queries in probabilistic relational databases have natural and important applications. To avoid unnecessary updates of probabilistic relational databases in existing general methods of assumption queries processing, an optimization method by computing conditional probability is proposed to handle assumption queries. The effectiveness of the optimization strategies for assumption queries is demonstrated in the experiment.
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Notes
- 1.
This research was supported by the Science Project of Department of Water Resources of Zhejiang Province [grant number RC1746]; the National Natural Science Foundation of China [grant number 61762055]; the Jiangxi Provincial Natural Science Foundation of China [grant number 20161BAB202036]; and the Jiangxi Provincial Social Science “13th Five-Year” (2016) Planning Project of China [grant number 16JY19].
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Zhang, C., Cui, Z., Yu, H. (2018). Assumption Queries Processing of Probabilistic Relational Databases. In: Li, K., Li, W., Chen, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2017. Communications in Computer and Information Science, vol 874. Springer, Singapore. https://doi.org/10.1007/978-981-13-1651-7_32
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