Here we introduce a data staging algorithm designed to reconstruct multiple time series databases into a partitioned and regularised database. The Data Aggregation Partition Reduction Algorithm, or DAPRA for short, was designed to solve the practical issue of effective and meaningful visualisation of irregularly sampled time series data. This paper firstly discusses the rationale for DAPRA, walking through its design and introduces the theoretical foundation of any DAPRA application. Later we report empirical evidence that demonstrates the practical relevance of DAPRA by its application with large and complex time series datasets from two distinct domains (financial and travel).