Datalog with External Machine Learning Functions for Automated Cloud Resource Configuration - ISWC 2023 Posters and Demos

Aug 5, 2026

Industry 4.0 and IoT create massive factory-production datasets, making big-data processing challenging for teams that are not cloud experts. Cloud platforms are increasingly needed to parallelize computation and reduce runtime, but deploying and tuning solutions typically requires extensive assistance. SemCloud addresses this by providing a semantics-enhanced cloud system evaluated on industrial scenarios with millions of data and thousands of repeated runs, enabling non-experts to configure resources via Datalog rules with external machine learning functions.

LanguageEnglish

CategoryTechnology

FormatPDF

Pages5

Reading Time13 min

AccessFree to download

Introduction

Background and Challenges

Our Approach

Approach

Use Case: Distributed Semantic ETL

KG Construction for ETL Pipelines

Datalog with External Machine Learning Functions for Automated Cloud Resource Configuration Zhuoxun Zheng1,2, Ognjen Savkovic3 , Nikolay Nikolov4,2, Luu Huu Phuc1 , Ahmet Soylu4,2, Evgeny Kharlamov4,2 and Baifan Zhou4,2 1 Bosch Center for AI, Germany 2 Department of Informatics, University of Oslo, Norway 3 Free University of Bozen-Bolzano 4 Department of Computer Science, Oslo Metropolitan University, Norway Abstract Industry 4.0 and Internet of Things (IoT) technologies unlock unprecedented amount of data from factory production, posing big data challenges. 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. To this end, we propose SemCloud, a semantics-enhanced cloud system, for tackling the challenges of data volume and more users. The system has been evaluated in industrial use case with millions of data, thousands of repeated runs, and domain users, showing promising results. This poster paper accompanies our full paper and focuses on Datalog rules with external machine learning functions for automated resource configuration, and provides additional discussion on formalism and implementation techniques. Keywords Datalog, knowledge graph, cloud configuration, machine learning 1. Introduction Background and Challenges. Industry 4.0 focuses on smart factories that rely on IoT technology for automation. This produces massive amounts of production data, increasing the demand for data-driven solutions and cloud technology. Yet, users of these solutions and cloud technology are often not cloud experts, such as domain experts and data scientists. In a standard setting of a data science project, the team requires extensive assistance from cloud experts, whenever they want to deploy solutions or make small changes to their solutions deployed on the cloud. To facilitate the adoption of cloud systems for more projects and users, one can equip all projects with some cloud experts, or launch training programs about cloud technology. Both require careful planing to balance time, cost, and benefits. Our Approach. We notice that the existing work on this topic addressed the cloud deployment issues only to a limited extent [1], whereby they either only focus on the formal description of cloud, or on the limited adaptability of cloud systems. To address scalability challenges in data ISWC 2023 Posters and Demos: 22nd International Semantic Web Conference, November 6 –10, 2023, Athens, Greece © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0) . CEUR [http://ceur-ws.org](http://ceur-ws.org) Workshop ISSN 1613-0073 Proceedings CEUR Workshop Proceedings ([CEUR-WS.org](CEUR-WS.org)) CEUR ~~ ~~[Workshop](Workshop ceur-ws.org)[ ceur-ws.org](Workshop ceur-ws.org)[ ](Workshop ceur-ws.org)[Proceedings](Proceedings ISSN 1613-0073)[ ISSN 1613-0073](Proceedings ISSN 1613-0073) volume and democratising cloud systems for more users, we propose SemCloud, a semanticsenhanced cloud system, that scales semantic ETL pipeline on the cloud, and allows non-cloud experts to deploy their solutions. A rough description of workflow of SemCloud is as follows: (0) non-cloud experts create knowledge graphs (KG) that represent ETL-Pipelines on a cloud system, where attributes of cloud resource configuration is under-specified; then Datalog rules execute in three steps: (i) graph extraction rules write populate rule predicates by extracting information from the ETL-pipeline KGs; (ii) resource estimation rules estimate the resource consumption for the given pipeline assuming that there is only one computing node (assuming infinite larg

FAQs

What problem does SemCloud address in cloud deployment for Industry 4.0 and IoT?

It targets the difficulty non-cloud experts face when deploying and adjusting data-driven solutions on cloud systems, especially when resource configuration details are under-specified.

How does SemCloud use Datalog rules within its cloud workflow?

Datalog rules run in three steps: graph extraction from ETL pipeline knowledge graphs, resource estimation for the pipeline, and resource configuration to find optimal distributed allocations.

What is the structure of the ETL pipeline used in the provided welding use case?

The ETL is decomposed into four steps: retrieve, slice, prepare, and store, where slicing splits data into subsets to enable parallel processing and storage.

Aug 5, 2026

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