This paper proposes a framework for mining sequential patterns in uncertain databases, with and without weight constraints, and an incremental approach for dynamic databases. The framework introduces multiple theoretically tightened pruning upper bounds to reduce the mining space and a novel hierarchical index structure, USeq-Trie, to efficiently maintain patterns. It also presents a faster method, SupCalc, for computing expected support and an efficient algorithm, FUSP, for mining sequential patterns in uncertain databases. Additionally, an incremental mining approach, InUSP, is proposed to handle dynamic databases by maintaining promising frequent sequences (PFS) to improve efficiency. The framework is validated through extensive experiments on real-life datasets, showing that it outperforms existing methods in terms of efficiency and completeness. The proposed techniques are effective for various applications involving uncertain sequential data, such as medical records, sensor networks, and user behavior analysis. The framework's use of USeq-Trie and PFS concepts enhances the effectiveness of incremental mining in uncertain databases.This paper proposes a framework for mining sequential patterns in uncertain databases, with and without weight constraints, and an incremental approach for dynamic databases. The framework introduces multiple theoretically tightened pruning upper bounds to reduce the mining space and a novel hierarchical index structure, USeq-Trie, to efficiently maintain patterns. It also presents a faster method, SupCalc, for computing expected support and an efficient algorithm, FUSP, for mining sequential patterns in uncertain databases. Additionally, an incremental mining approach, InUSP, is proposed to handle dynamic databases by maintaining promising frequent sequences (PFS) to improve efficiency. The framework is validated through extensive experiments on real-life datasets, showing that it outperforms existing methods in terms of efficiency and completeness. The proposed techniques are effective for various applications involving uncertain sequential data, such as medical records, sensor networks, and user behavior analysis. The framework's use of USeq-Trie and PFS concepts enhances the effectiveness of incremental mining in uncertain databases.