Employing Graph Databases as a Standardization Model towards Addressing Heterogeneity

Schema heterogeneity has been perceived as a major challenge towards data integration and exchange for more than two decades. The advent of big data and NoSQL data stores has further led to proliferation of data models thus exacerbating those challenges. It would be useful to have an approach that allows leveraging both schema-based and schemaless data stores. A graph model provides a solution towards unifying them under a common representation. We present an approach to transform schemas into a homogeneous graph representation so that they can be further integrated with data from other NoSQL stores in an automated and seamless manner. We demonstrate our approach over relational and RDF schemas but the framework is extensible to allow further integration of additional data stores. Through this paper, we contribute to the data complexity (variety) aspect of big data by bridging the gap between different data stores including schema-based and schemaagnostic. We utilize Neo4j as the graph database. We present a proof-of-concept that implements our approach along with an evaluation based on qualitative and quantitative metrics.