Transformation rules for decomposing heterogeneous data into triples

In order to fulfill the vision of a dataspace system, it requires a flexible, powerful and versatile data model that is able to represent a highly heterogeneous mix of data such as databases, web pages, XML, deep web, and files. In literature, the triple model was found a suitable candidate for a dataspace system, and able to represent structured, semi-structured and unstructured data into a single model. A triple model is based on the decomposition theory, and represents variety of data into a collection of triples. In this paper, we have proposed a decomposition algorithm for expressing various heterogeneous data models into the triple model. This algorithm is based on the decomposition theory of the triple model. By applying the decomposition algorithm, we have proposed a set of transformation rules for the existing data models. The transformation rules have been categorized for structured, semi-structured, and unstructured data models. These rules are able to decompose most of the existing data models into the triple model. We have empirically verified the algorithm as well as the transformation rules on different data sets having different data models.