Scaling Data Science Solutions with Semantics and Machine Learning: Bosch Case

TL;DR

SemCloud is a semantics-enhanced cloud system that integrates domain ontologies and machine learning to simplify data analysis and resource configuration for industrial IoT data, enabling non-cloud experts to efficiently deploy solutions.

Contribution

The paper introduces SemCloud, a novel system combining semantic technologies and machine learning to automate and simplify cloud-based data analysis in Industry 4.0 environments.

Findings

Efficient processing of millions of data points in industrial use cases.

Automated resource configuration reduces setup time for non-cloud experts.

Promising results in real-world industrial scenarios.

Abstract

Industry 4.0 and Internet of Things (IoT) technologies unlock unprecedented amount of data from factory production, posing big data challenges in volume and variety. In that context, distributed computing solutions such as cloud systems are leveraged to parallelise the data processing and reduce computation time. As the cloud systems become increasingly popular, there is increased demand that more users that were originally not cloud experts (such as data scientists, domain experts) deploy their solutions on the cloud systems. However, it is non-trivial to address both the high demand for cloud system users and the excessive time required to train them. To this end, we propose SemCloud, a semantics-enhanced cloud system, that couples cloud system with semantic technologies and machine learning. SemCloud relies on domain ontologies and mappings for data integration, and parallelises the semantic data integration and data analysis on distributed computing nodes. Furthermore, SemCloud adopts adaptive Datalog rules and machine learning for automated resource configuration, allowing non-cloud experts to use the cloud system. The system has been evaluated in industrial use case with millions of data, thousands of repeated runs, and domain users, showing promising results.