Probabilistic ranking in fuzzy object databases

Ranking queries have been investigated extensively in the past due to their broad range of applications. In this paper, we study this problem in the context of fuzzy objects that have indeterministic boundaries. Fuzzy objects play an important role in many areas, such as biomedical image databases and GIS. To the best of our knowledge, we present the first efficient approach for similarity ranking in fuzzy object databases. The main challenge of ranking fuzzy objects is that these objects consist of multiple instances, each associated with a probability. We propose a framework to transform fuzzy objects into probabilistic objects which can then be ranked using existing algorithms for probabilistic objects.

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