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Sequential pattern mining has become more and more popular in recent years due to its wide applications and the fact that it can find more information than association rules. Two famous algorithms in sequential pattern mining are AprioriAll and PrefixSpan. These two algorithms not only need to scan a database or projected-databases many times, but also require setting a minimal support threshold to prune infrequent data to obtain useful sequential patterns efficiently. In addition, they must rescan the database if new items or sequences are added. In this paper, we propose a novel algorithm called efficient sequential pattern enumeration (ESPE) to solve the above problems. In addition, our method can be applied in many applications, such as for the itemsets appearing at the same time in a sequence. In our experiments, we show that the performance of ESPE is better than the other two methods using various datasets.