A forensic-driven data model for automatic vehicles events analysis

Digital vision technologies emerged exponentially in all living areas to watch, play, control, or track events. Security checkpoints have benefited also from those technologies by integrating dedicated cameras in studied locations. The aim is to manage the vehicles accessing the inspection security point and fetching for any suspected ones. However, the gathered data volume continuously increases each day, making their analysis very hard and time-consuming. This paper uses semantic-based techniques to model the data flow between the cameras, checkpoints, and administrators. It uses ontologies to deal with the increased data size and its automatic analysis. It considers forensics requirements throughout the creation of the ontology modules to ensure the records’ admissibility for any possible investigation purposes. Ontology-based data modeling will help in the automatic events search and correlation to track suspicious vehicles efficiently.

Advances in an Event-Based Spatiotemporal Data Modeling

Spatiotemporal data are vitally important for the national economy and defense modernization since it is not only an important component of human society and geographical information of the environment but also a key carrier of spatiotemporal information. An event-based spatiotemporal data model and its improvements are employed to model spatiotemporal objects, change history, and change relation, which is the main approach to resolve the spatiotemporal change modeling and has been comprehensively developed in modeling theory and applications. This manuscript studies the event-based spatiotemporal data modeling theory based on three aspects of the cognitive theory, which are the spatiotemporal object, the concept of the spatiotemporal dynamic object, and the spatiotemporal object relationship. Then, the implementation characteristics of the models were analyzed regarding the management of cadastral information, analog natural disaster phenomena, and reasoning. Finally, the key points and difficulties of an event-based spatiotemporal data modeling and prospective developmental trends were discussed to provide insights with spatiotemporal data modeling.