A Systematic Literature Review on Ontology-driven Business Intelligence Components
Salima Zeroual et al.
What the paper says
This research undertakes a systematic literature review to explore the integration and application of ontologies within Business Intelligence (BI) components across a variety of domains. Ontologies, as formal representations of knowledge, have emerged as a key enabler in enhancing the functionality and intelligence of BI systems, particularly in the era of big data and digital transformation. The objective of this study is to analyze how ontologies are designed, implemented, and utilized to improve data integration, semantic interoperability, and system adaptability. The review draws upon data sources from Scopus, IEEE Explore, Science Direct, and Google Scholar, ensuring a rigorous and comprehensive coverage of relevant literature. Following a structured selection process based on inclusion and exclusion criteria, 27 peer-reviewed articles published between 2011 and 2024 were identified as meeting the quality and relevance standards for this study. The selected studies reveal that ontology-driven BI components offer several advantages, including the unification of heterogeneous data sources, improved semantic clarity, and enhanced reasoning capabilities for decision support. Moreover, ontologies contribute significantly to the flexibility and scalability of BI systems, facilitating the development of context-aware and domain-specific analytical tools. Despite these advantages, the review also highlights persistent challenges, such as difficulties in managing large-scale ontologies, real-time processing limitations, and organizational resistance to adoption due to complexity and integration costs. By synthesizing the existing body of knowledge, this review not only consolidates the current understanding of ontology-driven BI but also provides a conceptual framework for future research. It emphasizes the need for innovative approaches that address identified limitations and align ontology development with dynamic organizational requirements. The findings serve as a valuable resource for both researchers and practitioners, offering strategic insights into the design and deployment of advanced BI solutions. Ultimately, this study contributes to the evolving discourse on intelligent decision-making systems by bridging theoretical perspectives with real-world applications.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.