Identify hidden knowledge from sequence pattern with inaccurate data in some of real time applications. Frequentness of the pattern is measured on the basis of possible world semantics. Difficulties of probabilistic frequent sequence pattern are established in some of application datasets. U-Prefix span algorithm is implemented for avoiding the problem of “possible world semantics” Consider the problem of frequent sequence pattern in perspective of uncertain sequence data. Frequentness of pattern is defined on the form of possible world semantics. High quality patterns are effectively mined with respect to formal probabilistic model. U-prefix span algorithm is implemented to avoid the explosion of possible world semantics. Mining the probabilistic frequent data from uncertain sequence data for uncertain data models Mining of frequent item sets is one of the popular knowledge discovery and data mining tasks. The frequent item set mining algorithms find item sets from traditional transaction databases, in which the content of each transaction i.e. items is definitely known and precise. There are many real-life applications like location-based services, sensor monitoring systems in which the content of transactions is uncertain. This initiates the requirement of uncertain data mining. The frequent item set mining in uncertain transaction databases semantically and computationally differs from traditional techniques applied to standard certain transaction databases. The consideration of existential uncertainty of item sets, indicating the probability that an item set occurs in a transaction, makes the traditional techniques inapplicable.