The rise of Data Mesh as a paradigm for large-scale data management has challenged the traditional monolithic data lake approach by promoting decentralized ownership, domain-oriented architecture, and self-serve infrastructure. However, without a unified semantic layer, decentralization can lead to fragmentation, inconsistency, and governance bottlenecks. This paper proposes an ontology-guided approach to Data Mesh design, leveraging domain ontologies to ensure semantic interoperability, governance automation, and federated data product discovery. By aligning domain-specific knowledge structures with data product metadata, we demonstrate how ontologies facilitate coherent data governance across distributed teams while preserving the autonomy of individual domains. We explore architectural patterns, governance workflows, and implementation considerations, and present a case study to illustrate the application of this approach in a real-world enterprise setting.
Seppo Linnainmaa, A. Salomaa· International Journal of Dat...· 0 citations
Modern networks face increasing cyber threats such as malware, ransomware, phishing, DDoS, insider attacks, and advanced persistent threats, making traditional signature-based security systems less effective. Artificial Intelligence (AI), through machine learning and deep learning, enables intelligent threat detection by identifying known and unknown attacks in real time. This study proposes an AI-based threat detection framework that integrates network traffic analysis, preprocessing, feature engineering, threat classification, and automated response. Experimental evaluation using metrics such as accuracy, precision, recall, F1-score, false positive rate, and detection latency demonstrates that the proposed framework outperforms conventional methods by providing higher detection accuracy, lower false alarms, and faster response. Despite challenges related to data quality, model interpretability, and computational cost, AI-driven cybersecurity offers a scalable and effective solution for modern network security.
Seppo Linnainmaa, A. Salomaa· International Journal of Mod...· 0 citations
The proposed framework provides a scalable foundation for graph-based artificial intelligence and has applications in biomedical knowledge discovery, financial fraud detection, industrial digital twins, recommendation systems, cybersecurity intelligence, scientific literature mining, and smart governance.
Seppo Linnainmaa, A. Salomaa· International Journal of Eme...· 0 citations
