Managing Data, Information, and Technology in Cyber Physical Systems: Public Safety as a Service and its Systems

This paper introduces a unified representation for collaborative development of complex services in public safety to explain the infusion of digital technologies into the design of the community resilience processes with respect to the potential hazardous events. It aims at helping to understand how ICT-based knowledge may help stakeholders situated in various positions inside society to participate collaboratively to service development actions. It presents a roadmap to conceive, design, and operationalize the creation of digital artefacts to compound its supporting systems, including the digital one. This representation includes a way of describing the domain of interest to conceive complex services employing human-oriented development, a way of reasoning on the resilience processes complexity, using semantic reasoning along with the time series quality assurance (TSQA) solution ontology, and a way of developing data processing components as internal (technical) services in enterprise information systems to support the design of novel environmental monitoring digital services. A unified semantic reasoning-based approach to evaluate data quality in cyber-physical systems is described to exemplify the creation of a complex public service ecosystem that promotes collaborative knowledge sharing to formalize the domain expertise through the information intensive services. The TSQA ontology integrates knowledge from other domain-specific ontologies to define and share concepts designating observations acquired from sensors, quality issues, methods for detecting quality issues and correcting data, and tags applied to data objects to assure the data traceability. A semantic component that manages the TSQA ontology and the SWRL-encoded rules are introduced in the data acquisition module of a cyber-physical system application for environmental monitoring to solve a specific problem of data cleaning associated with the water resources management. This method is applicable to any time series of measured data.