A Data Provenance based Architecture to Enhance the Reliability of Data Analysis for Industry 4.0

Niggemann, Oliver

doi:10.1109/etfa.2018.8502519

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…Li et al extended the concept of data provenance in the manufacturing domain to acquire information about the data origin and data changes. They proposed an architecture to manage the provenance of process data, in which the data provenance is considered as annotation of process data [17]. Although the above studies suggest comprehensive provenance representations and extend considerations to databases for usage, they mainly process datasets by the query formulas defined by themselves instead of users' input.…”

Section: Related Workmentioning

confidence: 99%

Annolog: A Query Processing Framework for Modelling and Reasoning with Annotated Data

Zou¹,

Zou²,

Wang³

2023

Journal of Computers

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<p>Data annotation is the categorization and labelling of data for applications, such as machine learning, artificial intelligence, and data integration. The categorization and labelling are done to achieve a specific use case in relation to solving problems. Existing data annotation systems and modules face imperfections such as knowledge and annotation not being formally integrated, narrow application range, and difficulty to apply on existing database management applications. To analyze and process annotated data, obtain the relationship between different annotations, and capture metainformation in data provenance and probabilistic databases, in this paper, we design a back-end query processing framework as a supplementary interface for the database management system to extend operation to datasets and boost efficiency. The framework utilizes Java language and the MVC model for development to achieve lightweight, cross-platform, and high adaptability identities. The contribution of this paper is mainly reflected in two aspects. The first contribution is to implement query processing, provenance semiring, and semiring homomorphism over annotated data. The second contribution is to combine query processing and provenance with SQL statements in order to enable the database manager to invoke operations to annotation.</p> <p> </p>

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Section: Related Workmentioning

confidence: 99%

Annolog: A Query Processing Framework for Modelling and Reasoning with Annotated Data

Zou¹,

Zou²,

Wang³

2023

Journal of Computers

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“…analysis (Xu & Hua, 2017;Li & Niggemann, 2018). On the other hand, wireless communication are also an important factor in I4.0.…”

Section: Challenges Of Industry 40mentioning

confidence: 99%

“…Despite the benefits and advances promised by I4.0, the players in this arena have a wide range of challenges to cope with, from human-robot interaction (Jost et al, 2017;Calzado et al, 2018) to data analysis (Xu & Hua, 2017;Li & Niggemann, 2018). On the other hand, wireless communication are also an important factor in I4.0.…”

Section: Challenges Of Industry 40mentioning

confidence: 99%

Ontologies for Industry 4.0

Ragavan

Khamis

Fiorini³

et al. 2019

The Knowledge Engineering Review

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The current fourth industrial revolution, or 'Industry 4.0' (I4.0), is driven by digital data, connectivity, and cyber systems, and it has the potential to create impressive/new business opportunities. With the arrival of I4.0, the scenario of various intelligent systems interacting reliably and securely with each other becomes a reality which technical systems need to address. One major aspect of I4.0 is to adopt a coherent approach for the semantic communication in between multiple intelligent systems, which include human and artificial (software or hardware) agents. For this purpose, ontologies can provide the solution by formalizing the smart manufacturing knowledge in an interoperable way. Hence, this paper presents the few existing ontologies for I4.0, along with the current state of the standardization effort in the factory 4.0 domain and examples of real-world scenarios for I4.0.

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“…Hästbacka and Zoitl [8] introduce a conceptual architecture and an approach to use Semantic Web technologies to self-describe the capabilities and data provided by industrial devices and control systems. Li and Niggemann [9] propose a three-layered architecture with a central, ontology-based Modeling Layer in order to address data provenance problems. Gupta et al [10] present a system, KARMA, that utilizes mapping rules to translate structured data into RDF graphs.…”

Section: A Related Workmentioning

confidence: 99%

An Approach for an Automated Adaption of KPI Ontologies by Reusing Systems Engineering Data

Walzel

Vathoopan

Zoitl

et al. 2019

2019 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

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Technological progress leads to an increased utilization of data analysis and Business Intelligence that support manufacturing management decisions. Many promising solutions utilize semantic technologies. However, the deployment and maintenance of semantic technologies especially in reconfigurable manufacturing environments require a lot of manual effort. Concepts to embed them in an automated environment, as required by Reconfigurable Manufacturing Systems, are limited. In this paper, we present an approach to reuse systems engineering data to guide an automated process that updates a production data knowledge base. Thereby, an ontology that integrates distributed operational data to compute Key Performance Indicators such as the Overall Equipment Effectiveness index can be updated during the manufacturing reconfiguration process. This reduces the effort to handle the required changes of semantic data integration systems and enables a cost-effective adaption of the Business Intelligence for Reconfigurable Manufacturing Systems.

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A Data Provenance based Architecture to Enhance the Reliability of Data Analysis for Industry 4.0

Cited by 5 publications

References 14 publications

Annolog: A Query Processing Framework for Modelling and Reasoning with Annotated Data

Annolog: A Query Processing Framework for Modelling and Reasoning with Annotated Data

Ontologies for Industry 4.0

An Approach for an Automated Adaption of KPI Ontologies by Reusing Systems Engineering Data

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