Semantics and Usage Statistics for Multi-Dimensional Query Expansion
As the amount and complexity of data keeps increasing in data warehouses, their exploration for analytical purposes may be hindered. Recommender systems have grown very popular on the Web with sites like Amazon, Net ix, etc. These systems proved successful to help users explore available content related to what they are currently looking at. Recent systems consider the use of recommendation techniques to suggest data warehouse queries and help an analyst pursue its exploration. In this paper, the authors present a personalized query expansion component which suggests measures and dimensions to iteratively build consistent queries over a data warehouse.