In recent decades, several approaches have drawn analogies between pattern mining tasks and constraint programming. The development of modern constraint solvers (SAT, CP, Linear Programming, Answer Set Programming, etc.) has demonstrated the efficiency of these approaches on real-world datasets. More than the efficiency of declarative programming, we argue that the true benefit of this approach lies in its versatility and its ability to integrate expert knowledge. This is especially evident in the case of Answer Set Programming (ASP), originally designed as a tool for knowledge reasoning. In this presentation, we will focus on the task of sequential pattern mining, which involves discovering interesting patterns in a collection of sequences. Firstly, we will introduce the concept of ASP encoding for sequential pattern mining tasks and showcase the advantages of declarative programming in fast-prototyping complex mining tasks. Next, we will highlight the capability of the Clingo solver to efficiently combine reasoning and procedural programming to address the mining of sequential patterns. Finally, we will conclude this presentation by exploring the potential development of epistemic measures of interestingness at the crossroad of declarative pattern mining and knowledge reasoning. To illustrate the effectiveness of a knowledge-centric approach, we will provide practical examples of these advanced features, focusing specifically on their application in analyzing care pathways to answer pharmaco-epidemiological questions.