Probabilistic Ranking Queries on Gaussians

In many modern applications, there are no exact values available to describe the data objects. Instead, the feature values are considered to be uncertain. This uncertainty is modeled by probability distributions instead of exact feature values. A typical application of such an uncertainty model are moving objects where the exact position of each object can be determined only at discrete time intervals. Queries often involve the positions of objects between two such time stamps or after the last known time stamp. Then the objects are essentially uncertain unless the pattern of movement is very simple (e.g. linear). One of the most important probability density functions for those applications is the Gaussian or normal distribution which can be defined by a mean value and a standard deviation. In this paper, we examine a new type of queries on uncertain data objects, called probability ranking queries (PRQ). A PRQ retrieves those k objects which have the highest probability of being lo...

• Type: Conference
• Year: 2006
• Where: SSDBM
• Authors: Christian Böhm, Alexey Pryakhin, Matthias Schubert
• Keywords: Data Objects, Database, Feature Values, SSDBM 2006, Uncertain Data Objects