Autonomic Patterns: Modelling Data Killing Patterns Using High-Level Petri Nets

Pattern development for complex systems is a common practice nowadays. These patterns delineate the development of many systems, identifying behaviours and structures that can be re-used in several problems. Autonomic Data Killing Patterns represent a need when thinking about the final destination of data. Nowadays, the knowledge on data killing is as important as the knowledge on data storage. This work discusses this idea and shows patterns to deal with related issues. This approach can help organizations and people to control irrelevant data through the knowledge of different ways to eliminate them. The patterns are described using high- level Petri nets and active rules. This strategy allows to monitor and to act in the environment, an important characteristic of autonomic systems.