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

TL;DR

This paper introduces Radiant, a domain-specific language and architecture that enable domain experts to abstract low-level IoT sensor data into higher-level process activities for real-time monitoring in smart manufacturing and healthcare.

Contribution

The work presents a novel DSL called Radiant and a software architecture that facilitate online event abstraction from sensor streams for process activity detection in IoT systems.

Findings

Effective activity detection in IoT sensor streams

Potential for improving process monitoring accuracy

Applicability in smart manufacturing and healthcare

Abstract

Modern Internet of Things (IoT) systems are equipped with a large quantity 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 enable 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 that enables online event abstraction from IoT sensor streams using the CEP applications. We evaluate these applications to monitor activity executions in smart manufacturing and smart healthcare. These evaluations are useful to inform the domain expert about the quality of activity detections based on the specified patterns and potential for improvement via additional or modified patterns and sensors.