A probabilistic data model and algebra for location-based data warehouses and their implementation

This paper proposes a novel, probabilistic data model and algebra that improves the modeling and querying of uncertain data in spatial OLAP (SOLAP) to support location-based services. Data warehouses that support location-based services need to combine complex hierarchies, such as road networks or transportation infrastructures, with static and dynamic content, e.g., speed limits and vehicle positions, respectively. Both the hierarchies and the content are often uncertain in realworld applications. Our model supports the use of probability distributions within both facts and dimensions. We give an algebra that correctly aggregates uncertain data over uncertain hierarchies. This paper also describes an implementation of the model and algebra, gives a complexity analysis of the algebra, and reports on an empirical, experimental evaluation of the implementation. The work is motivated with a real-world case study, based on our collaboration with a leading Danish vendor of location-based services.