João Luiz Rebelo Moreira

Towards ontology-driven situation-aware disaster management

Over the last years, numerous ICT applications with mechanisms to detect situations have been developed to support disaster management (DM), which is a field of a great societal and economic importance. Those applications are termed situation-aware (SA) because they try, in near real-time, to perceive and comprehend a situation of some type (e.g. disease epidemics) and project a reaction to the detected situation (e.g. isolate diseased people). An obstacle to the modelling of SA applications is the lack of well-founded structural and temporal constructs, which is inherent to conventional design techniques. Ontology-driven conceptual modelling has been successfully applied to overcome this issue, where ontological analysis based on a foundational ontology supports the modelling of concepts within a specific field as a well-founded core ontology. In this paper we discuss the importance of a well-founded core ontology for DM to support the specification of SA applications. We give an overview of the comprehensive framework we are developing, in which the DM core ontology plays a prominent role in the development of SA applications. In particular, we discuss the challenge of harmonizing concepts related to the modelling of situations in a foundational ontology in the lights of the Barwisean situation theory, Situoid theory and Situation awareness theory. This challenge has to be addressed to properly support SA applications in DM.

Semantic Interoperability for the IoT: Analysis of JSON for Linked Data

Recently a number of IoT platforms have been developed to cope with the big data generated and consumed by sensors and actuators. Some IoT platforms achieve syntactic interoperability by adopting JavaScript Object Notation (JSON) to represent the message payload produced by devices.

Semantic Interoperability of Early Warning Systems: a Systematic Literature Review

An Early Warning System (EWS) is a system-of-systems that is able to detect emergency risks and warn different stakeholders. Semantic interoperability is crucial in these systems because it enables unambiguous data exchange, so that different data sources can be integrated with EWS. This chapter presents the results of our systematic literature review on EWS interoperability. The research questions targeted existing interoperable EWS and their architecture and components functions, emphasizing the role of data representation mechanisms, for example, data/message models, protocols and ontologies. Since semantic models rely on syntactical models, this review also includes these data representation approches. This chapter also analyzes the main issues concerning the improvement of EWS interoperability, stressing the role of the internet of Things in smart situation-aware emergency services. We conclude by proposing topics for follow-up research.

Ontology-Driven Conceptual Modeling for Early Warning Systems: Redesigning the Situation Modeling Language

An early warning system (EWS) is an integrated system that supports the detection, monitoring and alerting of emergency situations. A possible application of an EWS is in epidemiological surveillance, to detect infectious disease outbreaks in geographical areas. In this scenario, a challenge in the development and integration of applications on top of EWS is to achieve common understanding between epidemiologists and software developers, allowing the specification of rules resulted from epidemiological studies. To address this challenge this paper describes an ontology-based model-driven engineering (MDE) framework that relies on the Situation Modelling Language (SML), a knowledge specification technique for situation identification. Some requirements are realized by revisiting SML, which resulted in a complete redesign of its semantics, abstract and concrete syntaxes. The initial validation shows that our framework can accelerate the generation of high quality situation-aware applica tions, being suitable for other application scenarios.