Goal driven approach to model interaction between viewpoints of a multi-view KDD process

Knowledge Discovery in Databases (KDD) is a highly complex, iterative and interactive process, with a goal-driven and domain dependent nature. The complexity of KDD is mainly due to the nature of the analyzed data (which are massive, distributed, incomplete, and heterogeneous) and the nature of the process itself (since the process is by definition interactive and iterative). Given this complexity, a KDD user faces two major challenges: on the one hand, he must manipulate prior domain knowledge to better understand data and business objectives. On the other hand, he must be able to choose, configure, compose and execute tools and methods from various fields (e.g., machine learning, statistics, artificial intelligence, databases) to achieve goals. Furthermore, in the business real world, a data mining project is usually held by several actors (domain experts, data analysts, KDD experts ...), each with a different viewpoint. In this paper we propose to tackle the complexity of KDD process, and to enhance coordination and knowledge sharing between actors of a multi-view KDD analysis through a goal driven modeling of interactions between viewpoints. After a brief review of our approach of viewpoint in KDD, we will first develop a semantic Model of Goals that allows identification and representation of business objectives during the business understanding step of KDD process. Then, based on this Goal Model, we define a set of semantic relations between viewpoints of a multi-view analysis; namely equivalence, inclusion, conflict and requirement.