Uniformly Integrated Database Approach for Heterogenous Databases

The demands of more storage, scalability, commodity of heterogenous data for storing, analyzing and retrieving data are rapidly increasing in today data-centric area such as cloud computing, big data analytics, etc. These demands cannot be solely handled by relational database system (RDBMS) due to its strict relational model for scalability and adaptability. Therefore, NoSQL (Not only SQL) database called non-relational database is recently introduced to extend RDBMS, and now it is widely used in some software developments. As a result, it becomes challenges regarding how to transform relational to non-relational database or how to integrate them to achieve business purposes regarding storage and adaptability. This paper therefore proposes an approach for uniformly integrated database to integrate data separately extracted from individual database schema from relational and NoSQL database systems. We firstly try to map the data elements in terms of their semantic meaning and structures with the help of ontological semantic mapping and metamodeling from the extracted data. We then cover structural, semantical and syntactical diversity of each database schema and produce integrated database results. To prove efficiency and usefulness of our proposed system, we test our developed system with popular datasets in BSON and traditional sql format using MongoDB and MySQL database. According to the results compared with other proficient contemporary approaches, we have achieved significant results in mapping similarity results although running time and retrieval time are competitive with the others