A DataOps Toolbox Enabling Continuous Semantic Integration of Devices for Edge-Cloud AI Applications

2025-07-30

AI Summary

To address cross-domain data heterogeneity and semantic interoperability challenges in edge-cloud collaborative AI applications, this paper proposes a continuous semantic integration method. The approach unifies static metadata and dynamic data streams by integrating Semantic Web technologies—including ontology modeling and RDF/SPARQL—with low-code DataOps pipelines. It enables plug-and-play, dynamic coordination across diverse device types, heterogeneous communication protocols, and heterogeneous semantic models, while significantly improving data engineering efficiency via visual low-code tooling. Empirical deployments across industrial automation, intelligent transportation, and healthcare domains validate end-to-end data interoperability, semantic consistency, and system scalability. The framework delivers a reusable methodology and engineering infrastructure for achieving semantic interoperability in edge intelligence systems.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Data Mining & Knowledge Management: Semantic Web
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The implementation of AI-based applications in complex environments often requires the collaboration of several devices spanning from edge to cloud. Identifying the required devices and configuring them to collaborate is a challenge relevant to different scenarios, like industrial shopfloors, road infrastructures, and healthcare therapies. We discuss the design and implementation of a DataOps toolbox leveraging Semantic Web technologies and a low-code mechanism to address heterogeneous data interoperability requirements in the development of such applications. The toolbox supports a continuous semantic integration approach to tackle various types of devices, data formats, and semantics, as well as different communication interfaces. The paper presents the application of the toolbox to three use cases from different domains, the DataOps pipelines implemented, and how they guarantee interoperability of static nodes' information and runtime data exchanges. Finally, we discuss the results from the piloting activities in the use cases and the lessons learned.

Problem

• Enabling device collaboration for edge-cloud AI applications
• Addressing heterogeneous data interoperability in AI development
• Supporting continuous semantic integration of diverse devices

Innovation

• Semantic Web technologies for data interoperability
• Low-code mechanism for device configuration
• Continuous semantic integration for diverse devices