Directed acyclic graphs (DAGs) are used in many domains ranging from computer science to bioinformatics, including industry and geoscience. They enable to model complex evolutions where spatial objects (e.g., soil erosion) may move, (dis)appear, merge or split. We study a new graph-based representation, called attributed DAG (a-DAG). It enables to capture interactions between objects as well as information on objects (e.g., characteristics or events). In this paper, we focus on pattern mining in such data. Our patterns, called weighted paths, offer a good trade-off between expressiveness and complexity. Frequency and compactness constraints are used to filter out uninteresting patterns. These constraints lead to an exact condensed representation (without loss of information) in the single-graph setting. A depth-first search strategy and an optimized data structure are proposed to achieve the efficiency of weighted path discovery. It does a progressive extension of patterns based on database projections. Relevance, scalability and genericity are illustrated by means of qualitative and quantitative results when mining various real and synthetic datasets. In particular, we show how such an approach can be used to monitor soil erosion using remote sensing and geographical information system (GIS) data.

有向无环图（DAG）用于从计算机科学到生物信息学的许多领域，包括工业和地球科学。它们能够对复杂的演化建模，其中空间物体（例如土壤侵蚀）可能移动，（消失），合并，分裂。我们研究了一种新的基于图的表示形式，称为归因DAG（a-DAG）。它可以捕获对象之间的交互以及对象信息（例如特征或事件）。在本文中，我们专注于此类数据中的模式挖掘。我们的模式称为加权路径，可以在表现力和复杂度之间取得良好的平衡。频率和紧密度约束用于滤除无用的模式。这些限制导致在单图设置中出现精确的压缩表示（不丢失信息）。提出了深度优先搜索策略和优化的数据结构，以实现加权路径发现的效率。它根据数据库预测对模式进行逐步扩展。挖掘各种真实和合成数据集时，通过定性和定量结果说明了相关性，可伸缩性和通用性。特别是，我们展示了如何使用遥感和地理信息系统（GIS）数据将这种方法用于监测土壤侵蚀。