An Approach Towards Information Extraction Based on Partitioning
In this paper, the authors describe an approach for information extraction in which they partition the dimensions (attributes), i.e., a higher dimension of large data set can be transformed into relatively smaller subsets of data in certain numbers that might be processed easily. Many techniques have been proposed in this but works on single database. Here, they use heterogeneous database as large data set. Thereafter, based on the separation of dimensions, the discernible dataset of all data are computed so as to get their core attribute sets. Furthermore, the attribute reduction and data redundancy methods are used to obtain the partition results.