A Hybrid Data Model for the Assessment of Border Control Technologies

Abstract

The increasing demand for traveler clearance at international border crossing points (BCPs) has motivated research for finding more efficient solutions. Automated border control (ABC) is emerging as a solution to enhance the convenience of travelers, the throughput of BCPs, and national security. In recent years, focus has been placed on the use and acceptance of these technologies, in order to understand the barriers to use and the acceptance problems people experience. Despite the growing interest in novel Smart Border Control (SBC) technologies and in the assessment of their usability and acceptance, there are currently no standardized ways to capture the data needed to assess their acceptance. This paper provides a review concerning recent approaches in data and ontology modelling in addition to relevant to the border control domain data models and ontologies. We then explain in detail the application of a hybrid approach, both knowledge-based and data-driven, in order to derive the core entities, attributes and relationships among them. Moreover, we propose a data model which aims to capture the data needed for the border control technologies assessment. Finally, the data model is evaluated by domain experts and the results are presented and discussed.