Efficient Probabilistic Query Ranking in Uncertain Databases

Large databases with uncertainty became more common in many applications.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 and that are assumed to be mutually-exclusive. 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. The objective is to rank the uncertain data according to their distance to a reference object.In the existing system, a framework is used for efficient computation of probabilistic similarity ranking queries in uncertain vector databases, each object is ranked in object instance wise and it is clustered and again ranking to the objects is performed, which results in log-linear i.e, O(nlogn). In this paper, we propose the Radix algorithm in order to increase the performance to O(n).we theoretically as well as experimentally show that it reduces this to a linear-time complexity while having the same memory requirements, facilitated by incremental accessing of the uncertain vector instances in increasing order of their distance to the reference object.