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https://ceur-ws.org/Vol-2849/paper-15.pdf
Realizing FAIR Data in an Enterprise
Environment
Melanie Hackl1 and Gökhan Coskun1,2
1
Bayer AG, Berlin, Germany
2
Beuth Hochschule für Technik, Berlin, Germany
FAIR Data for Enterprises
Timely reaction to changing environments like markets and regulations is a se-
vere challenge for globally acting enterprises. Therefore, fast informed decisions
are essential for the success and the sustainability. Relevant data needs to be
available in the right place at the right time. Exactly this is addressed by the
FAIR Data principles by demanding data to be findable, accessible, interoper-
able and reusable data. However, since its first publication in 2016 [FOR18], it
remains an open question, how FAIR Data can be realized.
We are convinced that the set of Linked Data [Tim06] principles is the most
promising solution to realize FAIR. Firstly, globally unique identifiers as de-
scribed in the Findability aspect are realized in Linked Data by the consequent
adoption of the Uniform Resource Identifier (URI) standard. Secondly, universal
Accssibility through standard communication protocols is realized by making
use of http. Thirdly, Interoperability is supported by focusing on leveraging
RDF and SPARQL, which are developed for a distributed data space. And fi-
nally, as it is emphasized by its name, linking to existing datasets is the most
crucial part of Linked Data, which addresses the Reusability aspect of FAIR
Data.
Required Service in the Enterprise IT Landscape
In the controlled environment of an enterprise, IT systems need to provide an
accurate level of service quality and reliability. The four Linked Data principles
need to be accomplished by the following five additional aspects to realized FAIR
in an enterprise completely.
Persistent Identifier: In order to assign URI-based identifiers to enterprise
assets, an appropriate registration service is required. Public domain name sys-
tems are not suitable, since enterprise IT solutions need to be independent from
outside systems, as much as possible. Furthermore a policy has to exist, which
explains what happens if the identifier gets deprecated.
Metadata Service: Due to accountability and responsibility aspects, metadata
about existing datasets are crucial. Therefore, a metadata service has to be pro-
vided, which stores the metadata separately from the data and is available, even
if the dataset itself does not exist anymore.
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
2 Melanie Hackl and Gökhan Coskun
Metadata Ontology: Metadata is core to FAIR data. To achieve this with the
Linked Data principles, a metadata ontology is required. This must include prop-
erties like: Security classification, identifier of the resource, information about
the creator and about the provenance.
Search Engine: To finally make the (meta)data findable, a search engine is
required. In case all of this services are provided, a FAIRness degree of 100% is
achieved.
Internal Licensing and Certification Process: The certification should be
issued by an recognized body and expresses whether a resource is compliant. A
company would have to set up a process for the licences and certifications.
Evaluation and Conclusion
In order to evaluate to what extend Linked Data and the additional services
as described above are supporting the realization of the FAIR Data principles
we utilized the FAIR Data metrics [Mar18]. The following table illustrates the
comparisson of a dataset stored in a sheet, an RDF dataset and a dataset stored
in a triplestore supported with the above mentioned services fulfills the FAIR
Data metrics.
Fig. 1. Evaluation of the FAIRness degree with the FAIR Data Metrics
This evaluation demonstrates clearly that every aspect of the FAIR Data
metrics are fulfilled. However, the metrics themselves are still work in progress.
Therefore, this evaluation depends on the acceptance of these metrics. It might
worth repeating the evaluation, as soon as the FAIR Data metrics are finalized.
Realizing FAIR Data in an Enterprise Environment 3
References
[AH11] Dean Allemang and Jim Hendler. Semantic Web for the working Ontolo-
gist. Morgan Kaufmann, USA, 2. edition, 2011.
[AIe09] Sicilia MA. Astrova I. and Lytras M.D. (eds). Metadata and Semantics.
Springer, Boston, MA, Boston, 1. edition, 2009.
[BEH15] Bernhard Humm Börteçin Ege and Anatol Reibold (Hr). Corporate
Semantic Web. Springer Vieweg, Heidelberg, 1. edition, 2015.
[BHB09] C. Bizer, T. Heath, and T. Berners-Lee. Linked data - the story so far.
Int. J. Semantic Web Inf. Syst., 5(3):1â22, 2009.
[BLHL01] Tim Berners-Lee, James Hendler, and Ora Lassila. The semantic web.
Scientific American, 284(5):34–43, May 2001.
[CHLR+ 16] Goekhan Coskun, Ralf Heese, Markus Luczak-Rösch, Radoslaw
Oldakowski, Ralph SchÃfermeier, and Olga Streibel. Towards corporate
semantic web: Requirements and use cases. Technical report, Freie Uni-
versitat Berlin, 2016.
[FOR18] FORCE11. https://www.force11.org/group/fairgroup/fairprinciples,
2018. Accessed: 2018-06-01.
[FÃ16] Christian FÃrber. Data Quality Management with Semantic Technologies.
Springer Gabler, Wiesbaden, 1. edition, 2016.
[GGHC+ 16] I. Grangel-González, L. Halilaj, G. Coskun, S. Auer, D. Collarana, and
M. Hoffmeister. Towards a semantic administrative shell for industry 4.0
components. 10.1109/ICSC.2016.58, 2016.
[GOF18] GOFAIR. https://www.go-fair.org/fair-principles/, 2018. Accessed: 2019-
09-12.
[Kel13] Thomas Kelly. How semantic technology drives agile business. Cognizant,
2013.
[LSH+ 09] Atif Latif, Anwar Us Saeed, Partick Hoefler, Alexander Stocker, and Clau-
dia Wagner. The linked data value chain: A lightweight model for business
engineers. In Adrian Paschke, Hans Weigand, Wernher Behrendt, Klaus
Tochtermann, and Tassilo Pellegrini, editors, Proceedings of I-Semantics
2009. 5th International Conference on Semantic Systems, pages 568–577.
Journal of Universal Computer Science, 2009.
[Mar18] Mark D. Wilkinson, Susanna-Assunta Sansone, Erik Schultes, Pe-
ter Doorn, Luiz Olavo Bonino da Silva Santos and Michel Dumontier.
https://github.com/FAIRMetrics/Metrics/blob/master/MaturityIndicators/Gen1/ALL.pdf,
2018. Accessed: 2019-09-12.
[MNV+ 17] Barend Mons, Cameron Neylon, Jan Velterop, Luiz Olavo Bonino da
Silva Santos Michel Dumontier, and Mark D. Wilkinson. Cloudy, increas-
ingly fair; revisiting the fair data guiding principles for the european open
science cloud. Information Services Use, vol. 37, no. 1, pp. 49-56, 2017,
2017.
[SBF98] Rudi Studer, V. Richard Benjamins, and Dieter Fensel. Knowledge engi-
neering: Principles and methods, 1998.
[SS09] Steffen Staab and Rudi Studer. Handbook on Ontologies. SpringerVerlag
Berlin Heidelberg, Berlin, 2. edition, 2009.
[Tim06] Tim Berners-Lee. https://www.w3.org/DesignIssues/LinkedData.html,
2006. Accessed: 2019-09-12.
[W3C18] W3C. https://www.w3.org/2001/sw/RDFCore/Schema/200203/, 2018.
Accessed: 2019-09-12.
4 Melanie Hackl and Gökhan Coskun
[Wil16] M. D. et al. Wilkinson. The fair guiding principles for scientific data man-
agement and stewardship. Sci. Data 3:160018 doi: 10.1038/sdata.2016.18,
2016.
[Yu11] Liyang Yu. A Developer’s Guide to the Semantic Web. Springer, Heidel-
berg, 1. edition, 2011.