Tracking of visitors in indoor spaces has many applications, specifically when administrating large public areas, like airports and train stations. In this work we present a data warehouse solution, designed to store and facilitate analysis of large amounts of tracking data. The solution is designed specifically for a data set provided by Blip Systems A/S, tracking visitors in Copenhagen airport. We propose to utilise sequential pattern mining to precompute flow information and present three different approaches, to incorporating frequent patterns for flow analysis in the data warehouse. A prototype system is implemented to provide grounds for an extensive experimental study. The study discloses the strengths and weaknesses of the different approaches, and indicates in which cases the different approaches are applicable.