Finally, Chapter XVI introduces a calendar-based pattern mining that aims at identifying patterns on specific calendar partitions in continuous data streams. The authors present how a data warehouse approach can be applied to leverage calendar-based pattern mining in data streams and how the framework of the DWFIST approach can cope with tight time constraints imposed by data streams, keep storage requirements at a manageable level and, at the same time, support calendar-based frequent itemset mining. The minimum granularity of analysis, parameters of the data warehouse (e.g. mining minimum support) and parameters of the database (e.g. extent size) provide ways to tune the load performance.