In industry, society and science advanced software is used for solving planning, scheduling and resource allocation problems, collectively known as constraint satisfaction or optimization problems. At the same time, one continuously gathers vast amounts of data about these problems. This project starts from the observation that current software typically does not exploit such data to update schedules, resources and plans. It aims at developing a new approach in which gathered data is analysed systematically in order to dynamically revise and adapt constraints and optimization criteria. Ultimately, this could create a new ICT paradigm, called Inductive Constraint Programming, that bridges the gap between the areas of data mining and machine learning on the one hand, and constraint programming on the other hand. If successful, this would change the face of data mining as well as constraint programming technology. It would not only allow one to use data mining techniques in constraint programming to improve the formulation and solution of constraint satisfaction problems, but also to employ declarative constraint programming principles in data mining and machine learning.