Source-linked AI summary
Emergent Behavior and Uncertainty in IoT-Enhanced Business Processes: Challenges and Future Directions
Marco Pegoraro, Sara Pettinari, Ivan Compagnucci, Marco Franceschetti, Ronny Seiger
TL;DR
IoT-enhanced business processes challenge traditional BPM because runtime behavior emerges from heterogeneous, autonomous, and partially observable physical–digital interactions. The paper analyzes uncertainty representation, operationalization, and runtime management, identifying research gaps and recommendations for a future agenda.
Problem
Traditional BPM assumptions are challenged because IoT-enhanced process behavior emerges at runtime under partial observability, uncertainty, autonomous systems, and changing contexts.
Method
The paper uses a motivating smart-warehouse scenario and state-of-the-art analysis to structure challenges across uncertainty representation, operationalization, and runtime management.
Results
The analysis identifies open gaps in uncertainty handling, reusable IoT-process operationalization, and runtime management of emergent behavior, then outlines research recommendations.
Takeaways & Limitations
Future IoT-enhanced BPM research should connect structured process control with increasing constituent-system autonomy in uncertain real-world domains.
Takeaways & Limitations
The state of the art lacks shared practices and reusable mechanisms for systematically operationalizing IoT-enhanced processes across heterogeneous settings.
Abstract
from arXiv · showhide
IoT-enhanced business processes are characterized by high complexity due to heterogeneous actors, varying levels of autonomy among participating systems, continuously evolving execution contexts spanning the digital and physical worlds, and continuous event streams. In such settings, process behavior partially emerges only at runtime through complex interactions involving humans, IoT devices, physical objects, software systems, agents, and services. This complexity introduces partial observability, uncertainty, and runtime dynamics that are difficult to anticipate and that challenge traditional business process management (BPM) assumptions and systems. We discuss these challenges from three perspectives, addressing 1) uncertainty representation, 2) operationalization of IoT-enhanced processes, and 3) runtime management of emergent behavior. Based on a motivating scenario and an analysis of the state of the art, we identify open research gaps and outline short-, medium-, and long-term recommendations to shape a research agenda on emergent behavior in IoT-enhanced business processes.
1. Introduction
IoT-enhanced business processes are more dynamic and less predictable because heterogeneous physical and digital actors interact through uncertain data, changing conditions, real-time events, and autonomous decisions. The paper frames research needs around uncertainty representation, runtime operationalization, and management of emergent behavior.
- IoT-enhanced processes combine process instances, devices, software services, and intelligent agents across physical and digital worlds.
- Uncertain data, changing environments, real-time events, and autonomous IoT decisions make behavior harder to anticipate than in traditional BPM.
- Process performance and correctness depend on monitoring real-world events, adapting execution decisions, and coordinating heterogeneous components during unanticipated situations.
- It identifies mechanisms for uncertainty representation, runtime monitoring and control through self-adaptive models and digital twins, and reusable BPM extensions.
- The paper discusses evolving IoT-enhanced processes across modeling, operationalization, and runtime management of uncertainty and emergent behavior.
2. Motivating Scenario: Sources of Uncertainty
The smart warehouse scenario illustrates how intertwined processes, heterogeneous autonomous actors, external interactions, and partial observability generate uncertainty at runtime. No single process owner can fully anticipate, observe, or control the overall behavior.
- Warehouse operations emerge from intertwined processes and ad-hoc runtime interactions among workers, robots, information systems, services, agents, and IoT devices.
- External delivery actors may expose only partial process knowledge and context because of organizational boundaries, security, and privacy restrictions.
- Closed, self-managed systems pursue local goals through proprietary strategies, preventing a single process owner from fully anticipating, observing, or controlling behavior.
- Distributed control, heterogeneous autonomy, and external interactions make a complete and consistent global process state unavailable or unreconstructible.
3. Sub-Challenges and Problem Decomposition
The paper decomposes runtime emergence into challenges spanning modeling, operationalization, and execution. These include uncertain and incomplete event data, dynamic context-aware enactment, and adaptive feedback-loop management under changing system states.
- SC1 – Modeling: SC1 addresses modeling threats from emergent behavior within the BPM lifecycle.
- SC1 – Modeling: Noisy, delayed, missing, duplicated, misattributed, or ambiguous sensor events require models to represent confidence, provenance, ambiguity, and alternative interpretations.
- SC2 – Operationalization: IoT execution must handle continuous streams, heterogeneous actors, real-time interactions, unexpected events, and changing contextual information rather than static control flow.
- SC2 – Operationalization: Runtime event-driven execution creates challenges involving device unavailability, platform heterogeneity, communication, reliability, latency, coordination, and independently evolving components.
- SC3 – Runtime management: The execution phase is organized around MAPE-K feedback loops, whose monitoring, planning, and action may be constrained by incomplete knowledge and unforeseen effects.
4. State of the Art
The state of the art addresses uncertainty analysis, IoT-aware modeling, event processing, and BPM–IoT integration, but support remains fragmented. Major gaps concern unified uncertainty handling, reusable execution infrastructure, and linking emergent-behavior management to business processes.
- Uncertainty and dynamics: Existing approaches use ontologies, high-level events, and context models, but often treat context as external rather than as a first-class execution construct.
- Uncertainty and dynamics: Current techniques address metadata-augmented analysis and IoT data ambiguity, yet unified frameworks carrying uncertainty through repair, event-log construction, and process mining remain limited.
- Operationalization: 43 of 84 analyzed approaches focus only on design, while 41 also address enactment or execution, showing operationalization is recognized but not fully consolidated.
- Operationalization: Research integrates BPM engines with CEP, stream processing, contextual data, services, microservices, and IoT-aware execution frameworks.
- Operationalization: The literature lacks reusable, portable, runtime-aware infrastructures for heterogeneous devices, dynamic events, contextual changes, and execution-time uncertainty.
- Runtime management: Emergent-behavior research either detects behavior without extending to runtime reaction or addresses IoT-system adaptation without linking it to business processes.
5. Gaps and Opportunities
IoT-enhanced processes expose gaps in traditional BPM because behavior emerges at runtime under uncertainty, partial observability, and continuously changing context. The paper identifies opportunities spanning uncertainty representation, contextual modeling, event-stream integration, distributed execution, and governed adaptation.
- Traditional predefined process models cannot fully anticipate behavior shaped by device failures, human deviations, environmental changes, and unforeseen events.
- Uncertainty should be explicit across the BPM lifecycle and MAPE-K phases so systems can reason with incomplete or missing information.
- Context models should represent how location, device state, environment, resources, and human factors trigger, constrain, or redirect process behavior.
- Shared ontologies and taxonomies can connect sensor data, context, process concepts, and behavioral models while supporting interoperability and reproducible event-log construction.
- BPM engines need integration with continuous, heterogeneous, uncertain event streams and more distributed, modular execution across edge and personal devices.
- Runtime adaptation requires governed mechanisms because emergent behavior creates risks of inconsistency, constraint violations, and unintended behavior.
- Future architectures should support decentralized, dynamic, self-adaptive MAPE-K mechanisms, shared extension points, reusable patterns, and common world models while respecting privacy and security.
6. Recommendations
The paper proposes a staged research agenda that moves from clarifying foundations, to developing operational mechanisms, to establishing shared architectures and distributed meta-adaptive infrastructures. Recommendations address uncertainty, event-driven execution, context, adaptation, portability, and knowledge sharing.
- Short term: Short-term work should implement uncertainty-aware event recording and assess uncertainty propagation across modeling and MAPE-K feedback-loop phases.
- Short term: BPM engines should be benchmarked for IoT-driven, event-based execution and native interaction with CEP and stream-processing platforms, including edge resource impacts.
- Short term: Researchers should identify unpredictability sources, define first-class context models, and analyze incomplete, local, evolving knowledge and privacy-preserving digital twins.
- Short term: Runtime monitoring and adaptation requirements should be identified for processes operating in continuously evolving execution contexts.
- Medium term: Medium-term work should develop probabilistic and non-deterministic analysis, explicit adaptation policies, constraint preservation, rollback, and structured context integration.
- Medium term: Standardized modeling and execution extension points and low-footprint, standard-compatible BPM systems should support resource-constrained devices.
- Long term: Long-term research should integrate process-native MAPE-K loops with decentralized monitoring, privacy- and goal-oriented knowledge disclosure, and distributed world models.
- Long term: The agenda further calls for shared semantics, scalable Cloud-Edge architectures, reusable components, and distributed meta-adaptive infrastructures with real-time decision-making and actuation guarantees.
7. Conclusions
IoT-enhanced processes challenge traditional BPM because behavior emerges among heterogeneous actors and autonomous systems in evolving contexts. The paper analyzes gaps across uncertainty representation, operationalization, and runtime management, then proposes staged recommendations for adaptive, context-aware, interoperable processes.
- Emergent behavior in IoT-enhanced system-of-systems contexts involves partial observability and uncertainty that challenge traditional BPM assumptions.
- The analysis covers uncertainty representation, IoT-enhanced process execution, and runtime management of emergent behavior across the BPM lifecycle.
- The paper identifies research gaps and proposes short-, medium-, and long-term recommendations for more adaptive, context-aware, interoperable IoT-enhanced business processes.
Declaration on Generative AI
The authors used GPT-5.5 and Grammarly for grammar and spelling checks, and AI-generated Figure 1, while reviewing and editing the content themselves.
- GPT-5.5 and Grammarly were used for grammar and spelling checks during preparation of the work.
- Figure 1 was AI-generated, as stated in its caption.
- The authors reviewed and edited the content and took full responsibility for it.