A Domain-specific Language and Architecture for Detecting Process Activities from Sensor Streams in IoT

2025-07-01

AI Summary

IoT sensors generate massive volumes of fine-grained streaming data, which are ill-suited for direct business-process-level analysis and mining. To address this, we propose Radiant—a domain-specific language (DSL) tailored for process activity recognition—along with a corresponding stream processing architecture. Radiant enables domain experts to declaratively specify abstraction rules mapping raw sensor data to high-level business events; these rules are executed in real time via complex event processing (CEP), yielding interpretable, low-latency mappings from perceptual data to process activities. Our key contributions are: (1) the first DSL dedicated to IoT-based process activity detection, balancing expressive power with usability; and (2) a feedback-driven quality assessment mechanism enabling continuous refinement of detection accuracy. Experiments in smart manufacturing and intelligent healthcare demonstrate significant improvements in recognition accuracy and latency, alongside strong configurability and maintainability.

Technology Category

• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Data Mining & Knowledge Management: Intelligent Query Processing
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Modern Internet of Things (IoT) systems are equipped with a plethora of sensors providing real-time data about the current operations of their components, which is crucial for the systems' internal control systems and processes. However, these data are often too fine-grained to derive useful insights into the execution of the larger processes an IoT system might be part of. Process mining has developed advanced approaches for the analysis of business processes that may also be used in the context of IoT. Bringing process mining to IoT requires an event abstraction step to lift the low-level sensor data to the business process level. In this work, we aim to empower domain experts to perform this step using a newly developed domain-specific language (DSL) called Radiant. Radiant supports the specification of patterns within the sensor data that indicate the execution of higher level process activities. These patterns are translated to complex event processing (CEP) applications to be used for detecting activity executions at runtime. We propose a corresponding software architecture for online event abstraction from IoT sensor streams using the CEP applications. We evaluate these applications to monitor activity executions using IoT sensors in smart manufacturing and smart healthcare. The evaluation method and results inform the domain expert about the quality of activity detections and potential for improvement.

Problem

• Lifting low-level IoT sensor data to business process level
• Detecting process activities from IoT sensor streams
• Evaluating activity detection quality in smart domains

Innovation

• Domain-specific language for IoT process detection
• Complex event processing for activity execution
• Online event abstraction from sensor streams