An integrated approach for visual analysis of a multisource moving objects knowledge base

We present an integrated and multidisciplinary approach for analyzing the behavior of moving objects. The results originate from an ongoing research of four different partners from the Dutch Poseidon project (Embedded Systems Institute (2007)), which aims to develop new methods for Maritime Safety and Security (MSS) systems to monitor vessel traffic in coastal areas. Our architecture enables an operator to visually test hypotheses about vessels with time-dependent sensor data and on-demand external knowledge. The system includes the following components: abstraction and simulation of trajectory sensor data, fusion of multiple heterogenous data sources, reasoning, and visual analysis of the combined data sources. We start by extracting segments of consistent movement from simulated or real-world trajectory data, which we store as instances of the Simple Event Model (SEM), an event ontology represented in the Resource Description Framework (RDF). Next, we add data from the web about vessels and geography to enrich the sensor data. This additional information is integrated with the representation of the vessels (actors) and places in SEM. The enriched trajectory data are stored in a knowledge base, which can be further annotated by reasoning and is queried by a visual analytics tool to search for spatiotemporal patterns. Although our approach is dedicated to MSS systems, we expect it to be useful in other domains.