An approach to simulating and analysing sensor events in a boxed beef supply chain is presented. The simulation component reflects our industrial partner's transport routes and parameters under normal and abnormal conditions. The simulated transport events are fed into our situational awareness system for detecting temperature anomalies or potential box tampering. The situational awareness system features a logic-based modeling language and an inference engine that tolerates incomplete or erroneous observations. The paper describes the approach and experimental results in more detail.

The smooth operation of industrial or business enterprises rests on constantly monitoring, evaluating and projecting their current state into the near future. Such situational awareness problems are not well supported by today's software solutions, which often lack higher-level analytic capabilities. To address these issues we propose a modular and re-usable system architecture for monitoring systems in terms of their state evolution. As a main novelty, states are represented explicitly and are amenable to external analysis. Moreovoer, different state trajectories can be derived and analysed simultaneously, for dealing with incomplete or noisy input data. In the paper we describe the system architecture and our implementation of a core component, the state inference engine, through a shallow embedding in Scala. The implementation of our modelling language as an embeded domain-specific language grants the modeller expressive power and flexibility, yet allows us to abstract a significant part of the complexity of the model's execution into the common inference engine core.

Fusemate is a logic programming system that implements the possible model semantics for disjunctive logic programs. Its input language is centered around a weak notion of stratification with comprehension and aggregation operators on top of it. Fusemate is implemented as a shallow embedding in the Scala programming language. This enables using Scala data types natively as terms, a tight interface with external systems, and it makes model computation available as an ordinary container data structure constructor. The paper describes the above features and implementation aspects. It also demonstrates them with a non-trivial use-case, the embedding of the description logic ALCIF into Fusemate’s input language.

The paper introduces a knowledge representation language that combines the event calculus with description logic in a logic programming framework. The purpose is to provide the user with an expressive language for modelling and analysing systems that evolve over time. The approach is exemplified with the logic programming language as implemented in the Fusemate system. The paper extends Fusemate's rule language with a weakly DL-safe interface to the description logic ALCIF and adapts the event calculus to this extended language. This way, time-stamped ABoxes can be manipulated as fluents in the event calculus. All that is done in the frame of Fusemate's concept of stratification by time. The paper provides conditions for soundness and completeness where appropriate. Using an elaborated example it demonstrates the interplay of the event calculus, description logic and logic programming rules for computing possible models as plausible explanations of the current state of the modelled system.