Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

A Context-Aware Knowledge Graph Platform for Stream Processing in Industrial IoT

Monica Marconi Sciarroni, Emanuele Storti

TL;DR

A context-aware semantic platform for data stream management that unifies heterogeneous IoT/IoE data sources through a Knowledge Graph enabling formal representation of devices, streams, agents, transformation pipelines, roles and rights is proposed.

Abstract

Industrial IoT ecosystems bring together sensors, machines and smart devices operating collaboratively across industrial environments. These systems generate large volumes of heterogeneous, high-velocity data streams that require interoperable, secure and contextually aware management. Most of the current stream management architectures, however, still rely on syntactic integration mechanisms, which result in limited flexibility, maintainability and interpretability in complex Industry 5.0 scenarios. This work proposes a context-aware semantic platform for data stream management that unifies heterogeneous IoT/IoE data sources through a Knowledge Graph enabling formal representation of devices, streams, agents, transformation pipelines, roles and rights. The model supports flexible data gathering, composable stream processing pipelines, and dynamic role-based data access based on agents' contexts, relying on Apache Kafka and Apache Flink for real-time processing, while SPARQL and SWRL-based reasoning provide context-dependent stream discovery. Experimental evaluations demonstrate the effectiveness of combining semantic models, context-aware reasoning and distributed stream processing to enable interoperable data workflows for Industry 5.0 environments.

A Context-Aware Knowledge Graph Platform for Stream Processing in Industrial IoT

TL;DR

A context-aware semantic platform for data stream management that unifies heterogeneous IoT/IoE data sources through a Knowledge Graph enabling formal representation of devices, streams, agents, transformation pipelines, roles and rights is proposed.

Abstract

Industrial IoT ecosystems bring together sensors, machines and smart devices operating collaboratively across industrial environments. These systems generate large volumes of heterogeneous, high-velocity data streams that require interoperable, secure and contextually aware management. Most of the current stream management architectures, however, still rely on syntactic integration mechanisms, which result in limited flexibility, maintainability and interpretability in complex Industry 5.0 scenarios. This work proposes a context-aware semantic platform for data stream management that unifies heterogeneous IoT/IoE data sources through a Knowledge Graph enabling formal representation of devices, streams, agents, transformation pipelines, roles and rights. The model supports flexible data gathering, composable stream processing pipelines, and dynamic role-based data access based on agents' contexts, relying on Apache Kafka and Apache Flink for real-time processing, while SPARQL and SWRL-based reasoning provide context-dependent stream discovery. Experimental evaluations demonstrate the effectiveness of combining semantic models, context-aware reasoning and distributed stream processing to enable interoperable data workflows for Industry 5.0 environments.
Paper Structure (28 sections, 5 equations, 14 figures, 2 tables)

This paper contains 28 sections, 5 equations, 14 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Semantic data models for IoT
  4. Semantic monitoring ad processing
  5. Frameworks for IoT/IoE
  6. Frameworks for stream processing
  7. Foundational concepts
  8. Design principles
  9. Definitions
  10. Knowledge Graph representation
  11. Domain graph
  12. Stream gathering graph
  13. Stream transformation graph
  14. Stream Management Platform
  15. Data gathering
  16. ...and 13 more sections

Figures (14)

  • Figure 1: An overview of the SemIoE ontology module (in yellow: external classes).
  • Figure 2: Fragment of the domain/stream gathering KGs.
  • Figure 3: Stream gathering ontological module (in yellow: external classes. In green: class from SemIoE).
  • Figure 4: Stream transformation ontological module (in green: class from the Stream gathering module).
  • Figure 5: Fragment of stream transformation KG (in white: instances of other graphs).
  • ...and 9 more figures

Theorems & Definitions (7)

  • Definition 1: Stream Source
  • Definition 2: Stream
  • Definition 3: Stream Operator
  • Definition 4: Transformation Pipeline
  • Definition 5: Agent
  • Definition 6: Stream accessibility
  • Definition 7: Pipeline accessibility