A method for re-computing pre-computed query results stored at a database server, wherein the pre-computed query results are composed of a plurality of shares, wherein each share includes a certain number of pre-computed query results, wherein computation re-sources of a computation machine needed to re-compute a pre-computed query result of a first share depend on whether or not other pre-computed query results of the first share are re-computed during a given time interval forming a current re-computation cycle, characterized in that the method comprises, at the computation machine: - receiving a request to re-compute pre-computed query results of the first share; - determining a number of pre-computed query results in the first share to be re-computed in the current re-computation cycle based on a Reinforcement Learning algorithm, the determined number of pre-computed query results being limited by the computation machine's computation resources available during the given time interval, comprising: o retrieving, from a statistics server, an indication of pre-computed query results of the first share which have been re-computed in a previous time interval; o determining to apply an exploitation mode of the Reinforcement Learning algorithm or to apply an exploration mode of the Reinforcement Learning algorithm based on a value function of the Reinforcement Learning algorithm for the number of pre-computed query results indicated by the retrieved indication; - storing the determined number of pre-computed query results in the first share in the statistics server for a subsequent determination of the number of pre-computed query results to be re-computed in a subsequent computation cycle; and - re-computing the determined number of pre-computed query results in the first share during the re-computation cycle. - receiving a request to re-compute pre-computed query results of the first share; - determining a number of pre-computed query results in the first share to be re-computed in the current re-computation cycle based on a Reinforcement Learning algorithm, the determined number of pre-computed query results being limited by the computation machine's computation resources available during the given time interval, comprising: o retrieving, from a statistics server, an indication of pre-computed query results of the first share which have been re-computed in a previous time interval; o determining to apply an exploitation mode of the Reinforcement Learning algorithm or to apply an exploration mode of the Reinforcement Learning algorithm based on a value function of the Reinforcement Learning algorithm for the number of pre-computed query results indicated by the retrieved indication; o retrieving, from a statistics server, an indication of pre-computed query results of the first share which have been re-computed in a previous time interval; o determining to apply an exploitation mode of the Reinforcement Learning algorithm or to apply an exploration mode of the Reinforcement Learning algorithm based on a value function of the Reinforcement Learning algorithm for the number of pre-computed query results indicated by the retrieved indication; - storing the determined number of pre-computed query results in the first share in the statistics server for a subsequent determination of the number of pre-computed query results to be re-computed in a subsequent computation cycle; and - re-computing the determined number of pre-computed query results in the first share during the re-computation cycle.