Selectivity estimation of large multidimensional data warehouses using logical grid directory

We describe an implementation scheme for selectivity estimation using Multi Level Grid File (MLGF). The MLGF is a balanced, dynamic and hierarchical file structure that conforms to non uniform and correlated distribution. Our main goal is to develop a technique to determine the selectivity for a query from a large database where the grid directory is implemented logically without taking any physical storage. Using our implementation scheme, we compared the estimated selectivity and the storage requirement. We found low error rate for the estimated selectivity. We also estimate the overflow situation of a MLGF when the number of dimensions and length of a dimension is large. We found better results for our logical implementation when the over flow condition is concerned. We present extensive experimental results, validating our theoretical analysis and demonstrating the advantage of our technique when compared to complex selectivity estimation techniques using the Microsoft SQL Server.