The discovery of high-level long-term system behavior patterns is essential for several tasks such as system analysis, performance tuning, adaptive data placement in a cloud data centers. This paper describes techniques for mining long-term activities from database query logs. We describe algorithms for extraction of query groups with similar occurrence patterns, identification of periodic groups, and experimentally evaluate the correspondence of the query groups with business processes in the system.