Ontology Opportunities and Challenges: Discussions from Semantic Data Integration Perspectives

Abstract

With the tremendous growth of digital information, big data management has become an emerging research area in recent years. In big data, handling a variety of data is significantly similar to dealing with the volume and velocity-rich information. Big data often deals with the storage of redundant information in heterogeneous formats from multiple data sources. To effectively manipulate the information of big data, the organizations necessitate data integration among the variety of data sources. Most of the real-time applications heavily rely on the evolution of available heterogeneous information sources. Consequently, data integration deals with the heterogeneity in the syntactic, schematic, or semantic structure of big data sources. In essence, data integration meets the interoperability problems in the big data environment while mapping the data or schema. Ontology-based data integration has been emerged as an active research area to overcome these constraints. Semantic web technology plays a crucial role in managing the disparate big data sources. With the assistance of semantic knowledge, the previous research works integrate the disparate data sources. Thus, this work provides a brief survey of semantic data integration in a big data environment. Semantic data integration is the process of inter-linking the data based on the conceptual representation and the relationships of the data. This work describes the significance of ontology in heterogeneous data sources integration with the advantages of big data. Furthermore, it presents the use-case of the 360-degree view of the customer for semantic integration in a real-world scenario. Finally, it concludes that the ontology-based integration models offer potential benefits for big data manipulation in different application fields