Skip to main content
SpringerLink
Log in

Semantic Data Management for Experimental Manufacturing Technologies

  • Schwerpunktbeitrag
  • Published:
Datenbank-Spektrum Aims and scope Submit manuscript

Abstract

Experimental manufacturing technologies play a significant role in improving production processes. For example, the microwave assisted manufacturing of composites can save energy and reduce turn-around times compared to the traditional heating in ovens. However, since this technology is not yet well-understood, it requires more research and development activities (e.g., simulation or production experiments) to enable stable and efficient production with controlled product quality. These activities span multiple divisions over (possibly) multiple organizations and require close cooperation and communication. In addition this process proceeds in an iterative manner and produces a lot of data and documents on the way. From our practical experience working in such a project, we identified knowledge gaps and communication challenges, many of which can be overcome with the support of IT Knowledge Management Systems.

The contribution of this paper is twofold: first, we propose a system design and domain modeling framework for developing semantic data management systems. Second, we apply the framework to develop a system with state-of-the-art technology practices. The system enriches data with semantic metadata, and manages heterogeneous data forms (structured and unstructured) using polyglot persistence and a data lake. Moreover, the system is modular and service-oriented; it can easily integrate with existing systems in the manufacturing environment via HTTP interfaces. The example use case for the proposed system involves simulations and experiments on the microwave assisted heating of composites as carried out in the scope of the Horizon 2020 project “SIMUTOOL” (http://www.simutool.com).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. https://www.ncbi.nlm.nih.gov/genbank/.

  2. http://bexis2.uni-jena.de/.

  3. https://www.coreinformatics.com/products/core-sdms/.

  4. https://www.bosch-si.com/iot-platform/bosch-iot-suite/homepage-bosch-iot-suite.html

  5. https://cloud.google.com/solutions/iot/

  6. Take the text in brackets to be a unique string IDs.

  7. We use Apache Tika (https://tika.apache.org/) as our extractor; it is an open-source, easily extendable framework for extracting the metadata and contents of digital files.

  8. https://www.influxdata.com.

  9. http://opentsdb.net.

  10. At the time of developing the Monitoring System, no real sensor data was still available so we generated mock-data to experiment with the concepts of Dynamic Time Wrapping and the UI design of the monitoring system. We plan to incorporate sensor data in the next iteration of the Monitoring System.

References

  1. Alavi M et al (2001) Review: knowledge management and knowledge management systems: conceptual foundations and research issues. MIS Q 25(1):107–136

    Article  Google Scholar 

  2. Boudreau MC et al (1998) Going Global: using information technology to advance the competitiveness of the virtual transnational organization. Acad Manag Perspect 12(4):120–128

    Article  Google Scholar 

  3. Buneman P et al (2006) Provenance management in Curated databases. In: Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data SIGMOD ’06. ACM, New York, pp 539–550

    Chapter  Google Scholar 

  4. Cavalcante E et al (2015) An analysis of reference architectures for the Internet of things. In: Exploring component-based techniques for constructing reference architectures (cobRA). ACM, New York, pp 13–16

    Google Scholar 

  5. Chervenak A et al (2000) The data grid: towards an architecture for the distributed management and analysis of large scientific datasets. J Netw Comput Appl 23(3):187–200

    Article  Google Scholar 

  6. Civili C et al (2013) MASTRO STUDIO: managing ontology-based data access applications. Proceedings VLDB Endowment 6(12):1314–1317

    Article  Google Scholar 

  7. Coppens S et al (2013) Reasoning over SPARQL. In: Linked data on the web, vol 996. CEUR-WS, Rio de Janeiro

    Google Scholar 

  8. Daraio C et al (2016) The advantages of an Ontology-Based Data Management approach: openness, interoperability and data quality. Scientometrics 108(1):441–455

    Article  Google Scholar 

  9. Davenport TH et al (1998) Successful knowledge management projects. Sloan Manage Rev 39(2):43–57

    Google Scholar 

  10. Dean M et al (2004) OWL web ontology language reference

    Google Scholar 

  11. Gray J et al (2005) Scientific data management in the coming decade. SIGMOD Rec 34(4):34–41

    Article  Google Scholar 

  12. Jezek P, Moucek R (2015) Semantic framework for mapping object-oriented model to semantic web languages. Front Neuroinform 9:3

    Google Scholar 

  13. Kharlamov E et al (2016) Towards analytics aware ontology based access to static and streaming data. In: The semantic web – ISWC 2016. Lecture notes in computer science. Springer, Cham, pp 344–362

    Chapter  Google Scholar 

  14. Kharlamov E et al (2017) Ontology based data access in statoil. Web Semant 44:3–36

    Article  Google Scholar 

  15. Lee SM, Hong S (2002) An enterprise-wide knowledge management system infrastructure. Ind Manag Data Syst 102(1):17–25

    Article  Google Scholar 

  16. Li YF et al (2010) PODD – towards an extensible, domain-agnostic scientific data management system. In: IEEE Sixth International Conference on e‑Science. IEEE, Brisbane, pp 137–144

    Google Scholar 

  17. Chuttur MY (2013) Perceived helpfulness of dublin core semantics: an empirical study. In: Metadata and semantics research. MTSR 2013, vol 390. Springer, Cham, pp 135–145

    Chapter  Google Scholar 

  18. O’Leary D (1998) Enterprise knowledge management. Computer (Long Beach Calif) 31(3):54–61

    Google Scholar 

  19. Panchal PR, Swaminarayan R (2017) Execution of SPARQL query using apache Jena Fuseki server in AISHE domain. Int J Adv Eng Res Dev 4(9):387–394

    Google Scholar 

  20. Pawlowski A et al (2009) Simulation of greenhouse climate monitoring and control with wireless sensor network and event-based control. Sensors 9(1):232–252

    Article  MathSciNet  Google Scholar 

  21. Poggi A et al (2008) Linking data to ontologies. In: Journal on data semantics X. Lecture Notes in Computer Science. Springer, Berlin, Heidelberg, pp 133–173

    Chapter  Google Scholar 

  22. Rodriguez-Gonzalez A et al (2014) Special issue on exploiting semantic technologies with particularization on linked data over grid and cloud architectures. Future Gener Comput Syst 32(C):260–262

    Google Scholar 

  23. Sadalage PJ, Fowler M (2012) NoSQL distilled: a brief guide to the emerging world of polyglot persistence, 1st edn. Addison-Wesley, Boston

    Google Scholar 

  24. Schaarschmidt M, Gessert F et al (2015) Towards automated polyglot persistence. Workshop Paper, Datenbanksysteme für Business, Technologie und Web (BTW). Hamburg,

    Google Scholar 

  25. Sengstock C, Mathis C (2017) SAP HANA Vora: a distributed computing platform for enterprise data lakes. In: Mitschang B, et al (Eds) Datenbanksysteme für Business, Technologie und Web (BTW 2017). Lecture Notes in Informatics (LNI), Gesellschaft für Informatik e.V., Bonn, S 521

    Google Scholar 

  26. Steuer S et al (2016) Challenges and design goals for an architecture of a privacy-preserving smart city lab. Datenbank Spektrum 16(2):147–156

    Article  Google Scholar 

  27. Tonne A (2017) Big data is no longer equivalent to Hadoop in the industry. In: Mitschang B, et al (Eds) Datenbanksysteme für Business, Technologie und Web (BTW 2017). Lecture Notes in Informatics (LNI), Gesellschaft für Informatik e.V., Bonn, S 523–524

    Google Scholar 

  28. Carnegie Mellon University (2017) Data custodians – information security office - computing services – Carnegie Mellon University. https://www.cmu.edu/iso/governance/roles/data-custodian.html. Accessed: 19.01.2018

    Google Scholar 

  29. Carnegie Mellon University (2017) Data stewards – information security office – computing services – Carnegie Mellon University. http://www.cmu.edu/iso/governance/roles/data-steward.html. Accessed: 19.01.2018

    Google Scholar 

  30. NC State University (2016) REG 08.00.03 – data management procedures – policies, regulations & rules. https://policies.ncsu.edu/regulation/reg-08-00-03/. Accessed: 19.01.2018

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank the two anonymous reviewers for their valuable comments, as well as Eugene Yip and Rayan Alagbari for proofreading the paper in such a short notice.

Author information

Authors and Affiliations

  1. University of Bamberg, Bamberg, Germany

    Nasr Kasrin, Maliha Qureshi, Simon Steuer & Daniela Nicklas

Authors
  1. Nasr Kasrin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Maliha Qureshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Simon Steuer
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Daniela Nicklas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nasr Kasrin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kasrin, N., Qureshi, M., Steuer, S. et al. Semantic Data Management for Experimental Manufacturing Technologies. Datenbank Spektrum 18, 27–37 (2018). https://doi.org/10.1007/s13222-018-0274-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13222-018-0274-0

Keywords

Search

Navigation