[This post is based on Auke Hofman’s Master Information Science thesis].
The Dutch Customs Administration handles an immense volume of data daily, primarily for risk assessment and critical safety, health, economy, and environment (VGEM) tasks. However, as Auke Hofman highlights in his Master Information Science thesis, “Opportunities and Challenges for Linked Data at Customs Administration of The Netherlands,” the current focus on declaration data, rather than real-time container events, creates a significant bottleneck, limiting transparency and effectiveness.
Auke’s research dives deep into how Customs can dramatically improve its risk assessment by shifting its attention to these crucial events. His main objective was to explore the opportunities and challenges of using Linked Data to enhance local container tracking. By integrating diverse data sources through Linked Data principles, he aimed to provide a more holistic view.
His methodology employed the Design Science Research Methodology (DSRM), iteratively developing and evaluating a Container Tracking System. He prioritized key requirements using the MoSCoW method, ensuring that the most pressing needs were addressed first. The evaluation itself was framed around user stories, offering practical use cases and demonstrating the system’s potential. Auke built a prototype featuring two knowledge graphs with visualizations, data analysis capabilities, and a notification system. One graph was manually created, while the other leveraged the FEDeRATED prototype, a system designed for real-time data exchange between stakeholders. The evaluation successfully demonstrated the prototype’s ability to retrieve data from the FEDeRATED knowledge graph and apply complex business rules. While some user interface features were deprioritized, the focus shifted to incorporating machine learning algorithms and providing architectural views, illustrating how this innovative prototype could be seamlessly integrated into Customs’ existing infrastructure.
In conclusion, Auke Hofman’s thesis showcases, in a test environment, that Customs can significantly enrich container data by integrating it with other datasets using Linked Data principles. This not only allows for the application of sophisticated business rules but also paves the way for AI/ML-powered risk assessment capabilities such as anomaly detection and pattern extraction. His work emphasizes the transformative potential of Linked Data, while also acknowledging the essential need for manual effort in semantic data alignment before fully leveraging industry standards like FEDeRATED. This research marks a significant step towards a more intelligent and efficient Customs operation.
His thesis can be found below.
