To ensure the consistency of database subsystems involved in wireless communication systems, appropriate scheduled maintenance policies are necessary. However, the short-persistence of most of the data stored in the database and the highly dynamic evolution of the environmental conditions, which characterize such target systems, pose relevant issues in devising efficient maintenance policies. Aiming at deriving optimal maintenance strategies, this paper tackles the problem in two steps. First, a method is outlined to identify the most rewarding choice of audits frequency and combinations, given a setting for relevant parameters involved in the database of wireless communication systems (e.g., mean number of user calls and data corruption rates). Second, a learning approach is presented to dynamically adapt the maintenance policy at varying database and environmental parameter values leading to select, in each time period, the optimal maintenance policy.