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Provenance Data Models and Assertions: A Demonstrative Approach

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Part of the Studies in Computational Intelligence book series (SCI, volume 941)

Abstract

Provenance as perceived is a trail of a piece of a data item that helps in linking derived pieces of web resources or Internet of Everything (IoE) data to its creators. Provenance allows the software agents and developers to assert that devices across the IoE landscape can be made trustworthy if the same has a valid derivation path that is associated with it and is reliable/trustworthy. Provenance is considered as metadata that must be embedded into an OWL ontology, this metadata supports the semantic agents/reasoners to evaluate the data or workflow trail of the item in question. Contemporary researchers have proposed several models of trust that are based on mathematical calculations, however, the implementation of trust on semantically generated and modified documents i.e. ontologies at large is still evolving. This chapter thus aims to discuss, deliberate, and implement trust in an existing ontology using provenance assertions. This implementation of trust is based on the PROV-DM (Data Model) that has been suggested by the World Wide Web consortium. The chapter illustrates the implementation and inferencing of trust embedded in an OWL-based University Ontology. Provenance assertions using various scenarios and their inference have been highlighted to signify the validity and consistency of the ontology an XML serialized dataset.

Keywords

Provenance Scenario-based assertions Semantic IoT Interoperability Ontology Semantic web 

References

  1. 1.
    Ganzha, M., Paprzycki, M., Pawłowski, W., Szmeja, P., Wasielewska K.: “Towards Semantic Interoperability Between Internet of Things Platforms” Integration, Interconnection, and Interoperability of IoT Systems, Internet of Things (2018).  https://doi.org/10.1007/978-3-319-61300-0_6
  2. 2.
    Effective Design of Trust Ontologies for Improvement in the Structure of Socio-Semantic Trust NetworksGoogle Scholar
  3. 3.
    Pandey, M., Pandey, R.: Provenance constraints and attributes definition in OWL ontology to support machine learning. In: 2015 International Conference on Computational Intelligence and Communication Networks (CICN), pp. 1408–1414 (2015). Extracted from https://www.w3.org/TR/prov-primer/
  4. 4.
    Foster, I., Kesselman, C., Nick, J.M., Tuecke, S.: Grid services for distributed system integration. Computer 35, 37–46 (2002)CrossRefGoogle Scholar
  5. 5.
    Chen, L., Yang, X., Tao, F.: A semantic web service-based approach for augmented provenance. In: 2006 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2006 Main Conference Proceedings) (WI’06), pp. 594–600 (2006)Google Scholar
  6. 6.
    w3.org/TR/2020/CR-owl-time-20200326/, Time Ontology in OWL W3C Candidate Recommendation 26 Mar 2020Google Scholar
  7. 7.
    PROV-N: The Provenance Notation [Online]. Available https://www.w3.org/TR/prov-n/ (2013)
  8. 8.
    Tan, W.C.: Provenance in databases: past, current, and future. IEEE Data Eng. Bull. 30, 3–12 (2007)Google Scholar
  9. 9.
    Bose, R., Frew, J.: Lineage retrieval for scientific data processing: a survey. ACM Comput. Surveys (CSUR) 37, 1–28 (2005)CrossRefGoogle Scholar
  10. 10.
    Blount, M., Davis, J., Ebling, M., Kim, J.H., Kim, K.H., Lee, K., Misra, A., Park, S., Sow, D., Tak, Y.J.: Century: automated aspects of patient care. In: 13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA 2007), pp. 504–509 (2007)Google Scholar
  11. 11.
    Misra, A., Blount, M., Kementsietsidis, A., Sow, D., Wang, M.: Advances and challenges for scalable provenance in stream processing systems. In: International Provenance and Annotation Workshop, pp. 253–265 (2008)Google Scholar
  12. 12.
    Lakshmanan, G.T., Curbera, F.: Provenance in web applications. IEEE Internet Comput. 15(1), 17–21 (2011)Google Scholar
  13. 13.
    Glavic, B., Dittrich, K.R., Kemper, A., Schöning, H., Rose, T., Jarke, M., Seidl, T., Quix, C., Brochhaus, C.: Data provenance: a cctegorization of existing approaches. In: BTW’07: Datenbanksysteme in Buisness, Technologie und Web, pp. 227–241 (2007)Google Scholar
  14. 14.
    Khan, F.Z., et al.: Sharing interoperable workflow provenance: a review of best practices and their practical application in CWLProv.  https://doi.org/10.1093/gigascience/giz095
  15. 15.
    Ceolin, D., Groth, P.T., Van Hage, W.R., Nottamkandath, A., Fokkink, W.: Trust evaluation through user reputation and provenance analysis. URSW 900, 15–26 (2012)Google Scholar
  16. 16.
    Missier, P., Belhajjame, K., Cheney, J.: The W3C PROV family of specifications for modelling provenance metadata. In: EDBT’13 (2013)Google Scholar
  17. 17.
    Groth, P., Moreau, L.: PROV-Overview. An Overview of the PROV Family of Documents (2013)Google Scholar
  18. 18.
    Simmhan, Y.L., Plale, B., Gannon, D.: A survey of data provenance in e-science. ACM SIGMOD Record 34, 31–36 (2005)CrossRefGoogle Scholar
  19. 19.
    Cui, Y., Widom, J.: Practical lineage tracing in data warehouses. In: Proceedings of 16th International Conference on Data Engineering (Cat. No. 00CB37073), pp. 367–378 (2000)Google Scholar
  20. 20.
    Woodruff, A., Stonebraker, M.: Supporting fine-grained data lineage in a database visualization environment. In: Proceedings 13th International Conference on Data Engineering, pp. 91–102 (1997)Google Scholar
  21. 21.
    Buneman, P., Khanna, S., Wang-Chiew, T.: Why and where: a characterization of data provenance. In: International Conference on Database Theory, pp. 316–330 (2001)Google Scholar
  22. 22.
    Constraints of the PROV Data Model [Online]. Available https://www.w3.org/TR/2013/REC-prov-constraints-20130430/ (2013)
  23. 23.
    PROV-DM: The PROV Data Model [Online]. Available https://www.w3.org/TR/prov-dm/ (2013)
  24. 24.
  25. 25.
  26. 26.
    The W3C Working Charter [Online]. Available https://www.w3.org/2011/prov/wiki/Interoperability
  27. 27.
    OWL Web Ontology Language XML Presentation Syntax (2003)Google Scholar
  28. 28.
    Linking Across Provenance Bundles [Online]. Available https://www.w3.org/TR/2013/NOTE-prov-links-20130430/ (2013)
  29. 29.
    PROV-Dictionary: Modeling Provenance for Dictionary Data Structures [Online]. https://www.w3.org/TR/2013/NOTE-prov-dictionary-20130430/ (2013)
  30. 30.
  31. 31.
    PROV-AQ: Provenance Access and Query [Online]. Available https://www.w3.org/TR/2012/WD-prov-aq-20120619/ (2013)
  32. 32.
  33. 33.
  34. 34.
  35. 35.
    W3C. ProvONE: A PROV Extension Data Model for Scientific Workflow Provenance. Available Projects/job/ProvONE-Documentation-trunk/ws/provenance/ProvONE/v1/provonGoogle Scholar
  36. 36.
    Víctor Cuevas-Vicenttín, B.L., Ludäscher, B., Missier, P., Khalid Belhajjame, P., Fernando Chirigati, Y.W., Saumen Dey, U.D., Parisa Kianmajd, U.D., David Koop, S.B., Altintas, I., San Diego, U.C., Christopher Jones, M.B.J., Walker, L., Peter Slaughter, B.L., Yang Cao, U.: ProvONE: A PROV Extension Data Model for Scientific Workflow Provenance.Google Scholar
  37. 37.
  38. 38.
  39. 39.
  40. 40.
  41. 41.
    Kravari, K., Bassiliades, N.: ORDAIN: An Ontology for Trust Management in Theinternet of Things. https://www.researchgate.net/publication/320520680_ORDAIN_An_Ontology_for_Trust_Management_in_the_Internet_of_Things
  42. 42.
    Semantics of the PROV Data Model [Online]. Available https://www.w3.org/TR/prov-sem (2013)
  43. 43.

Copyright information

© Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.Amity Institute of Information TechnologyAmity University Uttar Pradesh Lucknow CampusLucknowIndia
  2. 2.AIIT, Amity University Lucknow CampusLucknowIndia

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