  Due to the rapid development of science and technology, the importance of
imprecise, noisy, and uncertain data is increasing at an exponential rate.
Thus, mining patterns in uncertain databases have drawn the attention of
researchers. Moreover, frequent sequences of items from these databases need to
be discovered for meaningful knowledge with great impact. In many real cases,
weights of items and patterns are introduced to find interesting sequences as a
measure of importance. Hence, a constraint of weight needs to be handled while
mining sequential patterns. Besides, due to the dynamic nature of databases,
mining important information has become more challenging. Instead of mining
patterns from scratch after each increment, incremental mining algorithms
utilize previously mined information to update the result immediately. Several
algorithms exist to mine frequent patterns and weighted sequences from
incremental databases. However, these algorithms are confined to mine the
precise ones. Therefore, we have developed an algorithm to mine frequent
sequences in an uncertain database in this work. Furthermore, we have proposed
two new techniques for mining when the database is incremental. Extensive
experiments have been conducted for performance evaluation. The analysis showed
the efficiency of our proposed framework.
