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

TL;DR

This paper presents a DataOps toolbox that uses Semantic Web technologies and low-code mechanisms to enable continuous semantic integration of heterogeneous devices across edge and cloud environments for AI applications.

Contribution

It introduces a novel DataOps toolbox that facilitates seamless integration and interoperability of diverse devices and data formats in complex AI-enabled systems.

Findings

Successful application in three diverse domain use cases

Guarantees interoperability of static and runtime data

Lessons learned from piloting activities

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.