Introducing an Enhanced Metadata Broker for Manufacturing Data Spaces
Authors: 
Alexandros Nizamis, Dimosthenis Ioannidis, Panagiotis Gkonis, Panagiotis TrakadasAuthors Info & Claims
Pages 37 - 40
Abstract
Nowadays, collaborative ecosystems and value networks have been established based on data sharing mechanisms and principles coming from concepts like Data Spaces. This data-centric approach has also increased the need for effective metadata management that enables entities participating in data sharing scenarios to find and trust available data. In this paper, a metadata broker for manufacturing related Data Spaces is introduced. It is based on an ontology that has been implemented to describe data related to Industries 4.0 and 5.0 implementations. The proposed broker is based on Data Spaces principles and artefacts that it extends by enabling semantic-based modeling and search capabilities.
References
[1]
Sebastian Bader, Jaroslav Pullmann, Christian Mader, Sebastian Tramp, Christoph Quix, Andreas W Müller, Haydar Akyürek, Matthias Böckmann, Benedikt T Imbusch, Johannes Lipp, 2020. The international data spaces information model–an ontology for sovereign exchange of digital content. In International Semantic Web Conference. Springer, 176–192.
[2]
Otto Borris, Mohrg Niko, Roggendorf Matthias, and Guggenberger Tobias. 2020. Data sharing in industrial ecosystems - Driving value across entire production lines. (Apr 2020), 9 pages.
[3]
Thomas Burns, John Cosgrove, and Frank Doyle. 2019. A Review of Interoperability Standards for Industry 4.0.Procedia Manufacturing 38 (2019), 646–653.
[4]
Qiushi Cao, Cecilia Zanni-Merk, and Christoph Reich. 2018. Ontologies for manufacturing process modeling: A survey. In International conference on sustainable design and manufacturing. Springer, 61–70.
[5]
Alberto Cotrino, Miguel A Sebastián, and Cristina González-Gaya. 2020. Industry 4.0 roadmap: Implementation for small and medium-sized enterprises. Applied sciences 10, 23 (2020), 8566.
[6]
Milos Drobnjakovic, Boonserm Kulvatunyou, Farhad Ameri, Chris Will, Barry Smith, and Albert Jones. 2022. The industrial ontologies foundry (IOF) core ontology. (2022).
[7]
Malte Hellmeier, Julia Pampus, Haydar Qarawlus, and Falk Howar. 2023. Implementing Data Sovereignty: Requirements & Challenges from Practice. In Proceedings of the 18th International Conference on Availability, Reliability and Security. 1–9.
[8]
Praveen Kumar Reddy Maddikunta, Quoc-Viet Pham, B Prabadevi, Natarajan Deepa, Kapal Dev, Thippa Reddy Gadekallu, Rukhsana Ruby, and Madhusanka Liyanage. 2022. Industry 5.0: A survey on enabling technologies and potential applications. Journal of industrial information integration 26 (2022), 100257.
[9]
Christoph Mertens, Jesús Alonso, Oscar Lázaro, Charaka Palansuriya, Gernot Böge, Alexandros Nizamis, Vaia Rousopoulou, Dimosthenis Ioannidis, Dimitrios Tzovaras, Rizkallah Touma, 2022. A framework for big data sovereignty: the European industrial data space (EIDS). In Data Spaces: Design, Deployment and Future Directions. Springer International Publishing Cham, 201–226.
[10]
Lars Nagel, Juan Jose Hierro, Eugenio Perea, Douwe Lycklama, Christoph Mertens, Anne-Sophie Taillandier, Maria Marques, Joshua Gelhaar, Angelo Marguglio, Ulrich Ahle, 2021. Design Principles for Data Spaces: Position Paper. Technical Report. E. ON Energy Research Center.
[11]
Alexandros Nizamis, Georg Schlake, Georgios Siachamis, Vasileios Dimitriadis, Christos Patsonakis, Christian Beecks, Dimosthenis Ioannidis, Konstantinos Votis, and Dimitrios Tzovaras. 2023. Designing a Marketplace to Exchange AI Models for Industry 5.0. In Artificial Intelligence in Manufacturing: Enabling Intelligent, Flexible and Cost-Effective Production Through AI. Springer Nature Switzerland Cham, 27–41.
[12]
Alexandros G Nizamis, Dimosthenis K Ioannidis, Nikolaos T Kaklanis, and Dimitrios K Tzovaras. 2018. A semantic framework for agent-based collaborative manufacturing eco-systems. IFAC-PapersOnLine 51, 11 (2018), 382–387.
[13]
Boris Otto. 2022. A federated infrastructure for European data spaces. Commun. ACM 65, 4 (2022), 44–45.
[14]
Julia Pampus, Brian-Frederik Jahnke, and Ronja Quensel. 2022. Evolving Data Space Technologies: Lessons Learned from an IDS Connector Reference Implementation. In International Symposium on Leveraging Applications of Formal Methods. Springer, 366–381.
[15]
Ricardo Silva Peres, Xiaodong Jia, Jay Lee, Keyi Sun, Armando Walter Colombo, and Jose Barata. 2020. Industrial artificial intelligence in industry 4.0-systematic review, challenges and outlook. IEEE access 8 (2020), 220121–220139.
[16]
Gustavo Correa Publio, Agnieszka Ławrynowicz, Larisa Soldatova, Pance Panov, Diego Esteves, Joaquin Vanschoren, and Tommaso Soru. 2020. ML-Schema: An interchangeable format for description of machine learning experiments. Semantic Web 0.0 (2020), 1–11.
[17]
Theofanis P Raptis, Andrea Passarella, and Marco Conti. 2019. Data management in industry 4.0: State of the art and open challenges. IEEE Access 7 (2019), 97052–97093.
[18]
Prashant Kumar Sinha, Sagar Bhimrao Gajbe, Kanu Chakraborty, Subhranshubhusan Sahoo, Sourav Debnath, and Shiva Shankar Mahato. 2021. A Review of Machine Learning Ontologies. International Journal of Information Dissemination and Technology 11, 4 (2021), 158–164.
[19]
Prashant Kumar Sinha, Sagar Bhimrao Gajbe, Sourav Debnath, Subhranshubhusan Sahoo, Kanu Chakraborty, and Shiva Shankar Mahato. 2022. A review of data mining ontologies. Data Technologies and Applications 56, 2 (2022), 172–204.
[20]
Gürkan Solmaz, Flavio Cirillo, Jonathan Fürst, Tobias Jacobs, Martin Bauer, Ernö Kovacs, Juan Ramón Santana, and Luis Sánchez. 2022. Enabling data spaces: Existing developments and challenges. In Proceedings of the 1st International Workshop on Data Economy. 42–48.
[21]
Usman Wajid, Alexandros Nizamis, and Victor Anaya. 2022. Towards Industry 5.0–A Trustworthy AI Framework for Digital Manufacturing with Humans in Control. Proceedings http://ceur-ws. org. ISSN 1613 (2022), 0073.
[22]
Mark D Wilkinson, Michel Dumontier, IJsbrand Jan Aalbersberg, Gabrielle Appleton, Myles Axton, Arie Baak, Niklas Blomberg, Jan-Willem Boiten, Luiz Bonino da Silva Santos, Philip E Bourne, 2016. The FAIR Guiding Principles for scientific data management and stewardship. Scientific data 3, 1 (2016), 1–9.
Index Terms
- Introducing an Enhanced Metadata Broker for Manufacturing Data Spaces
Recommendations
The MobiSpaces Manifesto on Mobility Data Spaces
eSAAM '24: Proceedings of the 4th Eclipse Security, AI, Architecture and Modelling Conference on Data SpaceData spaces consist of trusted frameworks that manage the entire data lifecycle, encompassing various data models, metadata descriptors, ontologies for semantic interpretation, and data services for accessing, processing, and analyzing data. Domain-...
Enabling data spaces: existing developments and challenges
DE '22: Proceedings of the 1st International Workshop on Data EconomyThis paper focuses on the concept of data spaces, which can serve as a basis for the future data economy. In data spaces, applicable to various business domains, stakeholders will be able to share data with each other in a controlled way. First, the ...
Data Trustees: A Whitelisting Approach for Trusted Data Sharing
eSAAM '24: Proceedings of the 4th Eclipse Security, AI, Architecture and Modelling Conference on Data SpaceIn an increasingly connected digital world, the strategic use of data is critical to improving predictive accuracy, optimizing processes, and driving innovative business models. Data spaces can facilitate sovereign and secure data sharing across ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
October 2024
118 pages
ISBN:9798400709845
DOI:10.1145/3685651
Copyright © 2024 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 22 October 2024
Check for updates
Author Tags
Qualifiers
- Short-paper
- Research
- Refereed limited
Funding Sources
Conference
eSAAM 2024
eSAAM 2024: 4th Eclipse Security, AI, Architecture and Modelling Conference on Data Space
October 22, 2024
Mainz, Germany
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 58Total Downloads
- Downloads (Last 12 months)58
- Downloads (Last 6 weeks)39
Reflects downloads up to 13 Dec 2024
Other Metrics
Citations
View Options
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML FormatLogin options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in