Time Granularities in Databases, Data Mining and Temporal Reasoning
Time Granularities in Databases, Data Mining and Temporal Reasoning
The description logic handbook: theory, implementation, and applications
The description logic handbook: theory, implementation, and applications
Pellet: A practical OWL-DL reasoner
Web Semantics: Science, Services and Agents on the World Wide Web
A conceptual view on trajectories
Data & Knowledge Engineering
Dynamic modeling of trajectory patterns using data mining and reverse engineering
ER '07 Tutorials, posters, panels and industrial contributions at the 26th international conference on Conceptual modeling - Volume 83
A Semantic Approach for the Modeling of Trajectories in Space and Time
ER '09 Proceedings of the ER 2009 Workshops (CoMoL, ETheCoM, FP-UML, MOST-ONISW, QoIS, RIGiM, SeCoGIS) on Advances in Conceptual Modeling - Challenging Perspectives
A conceptual data model for trajectory data mining
GIScience'10 Proceedings of the 6th international conference on Geographic information science
Scalable Detection of Spatiotemporal Encounters in Historical Movement Data
Computer Graphics Forum
| Hi-index | 0.00 |
About 90% of the world's cargo is transported in maritime containers, but less than 2% is physically inspected by custom authorities. The standard method to handle this problem consists in document-based risk analysis and route-based risk indicators to target anomalies. In this paper, we exploit a logic based approach to identify suspicious patterns in container itineraries. Specifically, we present an ontology to explicitly formalize the knowledge of the maritime container domain and a formalisation of two suspicious movement patterns to enable their discovery in a knowledge base. The formalisation can be extended to support the discovery of other itinerary patterns. Furthermore, the approach we present can be the basis for future development towards the formalisation and search of patterns in itineraries.