Classification of correlated subspaces using HoVer representation of Census Data

Sparse data are becoming increasingly common and available in many real-life applications. However, relatively little attention has been paid to effectively model the sparse data and existing approaches such as the conventional “horizontal” and “vertical” representations fail to provide satisfactory performance for both storage and query processing, as such approaches are too rigid and generally do not consider the dimension correlations. So a new technique called HoVer was proposed by Bin Cui. This method holds better than both horizontal and vertical representations. In this paper Census Data in sparse form are taken. The variations in performance in time, space and transactions are measured. The parameters are then compared with the performance in time, space and transactions measured for the E-commerce datasets. The changes in parameters with the change in schema are analyzed and the variations are observed.