event-driven processing

Traditional Business Process Management (BPM) struggles to unify discrete events with continuous sensor signals from Cyber-Physical Systems (CPS). Existing Signal Temporal Logic (STL)-based hybrid declarative approaches support only retrospective monitoring and lack real-time execution capabilities. To address this, we propose the first three-layer architecture enabling real-time execution of hybrid declarative processes, deeply integrating STL into a Complex Event Processing (CEP) engine. This integration supports joint temporal constraints over discrete events and real-valued signals, proactive activity triggering, and dynamic enforcement of process boundaries. Our approach achieves, for the first time in BPM, STL-driven online execution and closed-loop control—bridging the semantic and execution gap between declarative modeling and real-time physical-world operation. We validate its effectiveness and scalability in智能制造 and Industrial IoT scenarios.

Event-triggered systems suffer from interrupt storms and degraded timeliness/safety when operating beyond their safe envelope—i.e., under unanticipated environmental conditions. To address this, we propose an importance-driven robust scheduling framework. Our approach introduces a novel “task importance” dimension—orthogonal to both priority and criticality—and integrates mixed-criticality principles to jointly model environmental assumptions and regulate interrupt traffic. We formally verify schedulability using rigorous real-time analysis techniques. Experimental evaluation demonstrates that the framework significantly enhances guarantee rates for critical tasks under anomalous workloads, substantially narrowing the robustness gap between event-triggered and time-triggered systems. This work establishes a new paradigm for designing real-time systems resilient to environmental uncertainty.

Autonomous Driving Systems (ADS) and Advanced Driver Assistance Systems (ADAS) must comply with stringent end-to-end timing constraints mandated by international standards such as ISO 26262 and UN-R155; however, existing approaches lack transparent, white-box modeling and verifiable analysis of timing behavior across the full perception–planning–control–human–machine interaction pipeline. This paper proposes an event-chain-based white-box timing modeling and analysis framework that, for the first time, tightly couples functional safety requirements with regulatory timing constraints. The method enables architecture-level end-to-end timing constraint derivation, probabilistic timing verification, and timing-aware parameter optimization. Through model-based simulation and case studies, it facilitates early detection of timing violations, significantly improves design efficiency, and generates auditable, quantitative evidence of compliance—establishing a novel paradigm for standards conformance certification.

Existing world models struggle to simultaneously ensure reliability, verifiability, and online adaptability in complex environments governed by discrete events. This work proposes an intermediate approach grounded in natural language specifications: leveraging large language models to iteratively generate discrete-event world models that conform to the DEVS (Discrete Event System Specification) formalism, thereby decoupling model structure from behavior. The generated models are validated against semantic and temporal constraints derived from structured event trajectories, ensuring consistency over long-horizon simulations. This framework supports observable behaviors, reproducible verification, localized diagnostics, and efficient online deployment, offering a principled pathway toward trustworthy and adaptive world modeling in discrete-event settings.

This work addresses the challenges in industrial scheduling where asynchronous event streams often lead to inconsistent decision states, ambiguous action validity, and difficulties in attributing execution errors in reinforcement learning policies. To resolve these issues, the paper proposes a policy-decoupled execution and measurement layer that bridges the policy and the execution environment. By constructing valid decision snapshots, defining standardized execution contracts, and recording multidimensional execution deviations, the approach structurally formalizes execution semantics for the first time. This enables observable and attributable deployment discrepancies between simulation and reality, transforming ambiguous execution failures into type-labeled supervisory signals. Experimental results demonstrate that the framework consistently enhances diagnostic capability across varying observation delays, significantly reducing avoidable errors under low-latency conditions and providing structured supervisory data for policy evaluation and optimization.

This work addresses the limitation of existing formal methods, which are predominantly used for post-hoc verification, and the difficulty of modeling timing constraints in Event-B despite its support for correctness-by-construction. To bridge this gap, the paper proposes a non-intrusive, tool-supported mechanism for embedding time semantics into Event-B. By introducing clock variables and adopting timed automaton semantics, the approach seamlessly extends Event-B’s refinement framework to accommodate real-time constraints, while leveraging Event-B’s expressive first-order logic and set theory for precise modeling. Case studies demonstrate that the method effectively enables the systematic derivation of Timed Event-B models from timed automata, significantly enhancing the stepwise development and verification of complex real-time systems.

This study addresses behavioral inconsistencies in event-driven block-based programming environments like Scratch, which arise from non-deterministic scheduling orders. The work presents the first formalization of their concurrent semantics and scheduling space, introducing a parameterized framework for analyzing scheduling robustness. Leveraging dependency equivalence classes, partial-order reduction, and observation lens lattices, the authors implement SchedCheck—an analysis tool integrated into the actual Scratch virtual machine—that efficiently enumerates execution traces and compares cross-schedule behaviors. Empirical evaluation reveals that 21% of 224 student projects exhibit scheduling sensitivity, with a reproducibility rate of 17.6% in sampled public projects. SchedCheck successfully detects all 32 benchmark faults and identifies four incompletenesses in existing dependency models, uncovering widespread scheduling fragility in block-based programs.

Existing runtime enforcement techniques struggle to handle reactive systems with complex continuous dynamics and lack effective mechanisms for intervening in hybrid behaviors. This work proposes the first framework that integrates hybrid automata into runtime enforcement, enabling coordinated discrete event editing and continuous-time monitoring to correct system behavior at any instant by suppressing, delaying, or inserting events. The paper establishes formal enforceability conditions and devises an online strategy synthesis algorithm based on reachability analysis. Evaluation on an adaptive cruise control case study demonstrates that the approach ensures safety properties even when the underlying controller is unsafe, all while incurring minimal computational overhead.