Development of a Novel Semantic-Based System Integration Framework

It is a frequent problem in system development when various different systems with similar functionality have to be integrated together. System integration generally means accessing, sharing and exchanging data among applications. This data sharing can be achieved by implementing interfaces for communication and data exchange, and services for all the applications to be integrated. However, this solution requires a large amount of human resources for software development and refactoring. Here, a novel framework is proposed for data integration that makes the process much simpler by generating all the source code needed for querying data of an integrated system. The proposed framework is based on two ontology mediators, namely, a local ontology generated for the data source to be integrated, and a global or central ontology for the common query interface. This way, the integration process is reduced to finding automatically or semi-automatically the mapping between the concepts of local and global ontologies. If this semantic matching is performed, then all the predefined SPARQL queries of global ontology will be rewritten automatically to queries belonging to data sources to be integrated. Hence, the differences between the systems to be integrated will appear just in the DAO (Data Access Object) layer that is generated according to the ontology alignments. The proposed integration technique does not need any program coding in practice (only visual concept matching has to be done semi-automatically), so the productivity of integration software development can be improved significantly. To demonstrate our concept, our novel framework is applied to real-world integration tasks. In this paper, the framework, methodology, experiences and some measurements relating to the productivity will be presented and discussed.