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An Ontological Model and Services for Capturing and Tracking Provenance in Decentralized Social Networks

Published:05 November 2021Publication History

ABSTRACT

The rise of Decentralized Online Social Networks (DOSNs) and the increase in the number of active users on these networks offer an opportunity to develop solutions related to verifying origin, description paths and indicating the trajectory of the data that traffic in these networks. Provenance information is a key aspect of social networks because it is possible to evaluate the authenticity, reliability, and relevance of the information through its results. The speed of information generation and sharing, the decentralized storage strategy associated with the large volume of data represents a challenge for data provenance. Thus, this paper proposes DOSNPROV, a data provenance ontological model based on the W3C PROV-O specification. In addition, this paper proposes services based on DOSN-PROV model to support capture and tracking of provenance information in DOSNs. We evaluated DOSN-PROV model in two stages and demonstrated its quality and compliance with the proposed domain. The services underwent an evaluation of their performance and their results indicated acceptable response times.

References

  1. Sheetal Arya, Kumar Abhishek, and Akshay Deepak. 2019. Organizational Digital Footprint for Traceability, Provenance Approach. In Emerging Research in Computing, Information, Communication and Applications. Springer, 265--275.Google ScholarGoogle Scholar
  2. Leila Bahri, Barbara Carminati, and Elena Ferrari. 2018. Decentralized privacy preserving services for online social networks. Online Social Networks and Media 6 (2018), 18--25.Google ScholarGoogle ScholarCross RefCross Ref
  3. Geoffrey Barbier, Zhuo Feng, Pritam Gundecha, and Huan Liu. 2013. Provenance data in social media. Synthesis Lectures on Data Mining and Knowledge Discovery 4, 1 (2013), 1--84.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Sonja Buchegger, Doris Schiöberg, Le-Hung Vu, and Anwitaman Datta. 2009. PeerSoN: P2P social networking: early experiences and insights. In Proceedings of the Second ACM EuroSys Workshop on Social Network Systems. ACM, 46--52.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Ben De Meester, Anastasia Dimou, Ruben Verborgh, and Erik Mannens. 2017. Detailed provenance capture of data processing. In 1e Workshop on Enabling Open Semantic Science co-located with 16th International Semantic Web Conference. 1--8.Google ScholarGoogle Scholar
  6. Andrea De Salve, Barbara Guidi, and Paolo Mori. 2018. Predicting the availability of users' devices in decentralized online social networks. Concurrency and Computation: Practice and Experience 30, 20 (2018), e4390.Google ScholarGoogle ScholarCross RefCross Ref
  7. Takanori Fujiwara, Tarik Crnovrsanin, and Kwan-Liu Ma. 2018. Concise provenance of interactive network analysis. Visual Informatics 2, 4 (2018), 213--224.Google ScholarGoogle ScholarCross RefCross Ref
  8. Y Gil, J Cheney, P Groth, O Hartig, S Miles, L Moreau, and PP da Silva. 2010. Provenance XG final report, W3C provenance incubator group.Google ScholarGoogle Scholar
  9. Paul Groth, Yolanda Gil, James Cheney, and Simon Miles. 2012. Requirements for provenance on the web. International Journal of Digital Curation 7, 1 (2012), 39--56.Google ScholarGoogle ScholarCross RefCross Ref
  10. Matthias Häsel and Karsten Rieke. 2009. OpenSocial. Informatik-Spektrum 32, 3 (2009), 250--254.Google ScholarGoogle ScholarCross RefCross Ref
  11. Chunhyeok Lim, Shiyong Lu, Artem Chebotko, and Farshad Fotouhi. 2010. Prospective and retrospective provenance collection in scientific workflow environments. In 2010 IEEE International Conference on Services Computing. IEEE, 449--456.Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Sara Magliacane. 2012. Reconstructing provenance. In International Semantic Web Conference. Springer, 399--406.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Paolo Missier. 2016. The lifecycle of provenance metadata and its associated challenges and opportunities. In Building Trust in Information. Springer, 127--137.Google ScholarGoogle Scholar
  14. Luc Moreau. 2010. The foundations for provenance on the web. Foundations and Trends in Web Science 2, 2-3 (2010), 99--241.Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. María Poveda-Villalón, Asunción Gómez-Pérez, and Mari Carmen Suárez-Figueroa. 2014. Oops!(ontology pitfall scanner!): An on-line tool for ontology evaluation. International Journal on Semantic Web and Information Systems (IJSWIS) 10, 2 (2014), 7--34.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. María Poveda-Villalón, Mari Carmen Suárez-Figueroa, and Asunción Gómez-Pérez. 2012. Validating ontologies with oops!. In International conference on knowledge engineering and knowledge management. Springer, 267--281.Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Mayank Rawat and Ajay Kshemkalyani. 2003. SWIFT: Scheduling in web servers for fast response time. In Second IEEE International Symposium on Network Computing and Applications, 2003. NCA 2003. IEEE, 51--58.Google ScholarGoogle ScholarCross RefCross Ref
  18. Mirela Riveni, Tien-Dung Nguyen, Mehmet S Aktas, and Schahram Dustdar. 2019. Application of provenance in social computing: A case study. Concurrency and Computation: Practice and Experience 31, 3 (2019), e4894.Google ScholarGoogle ScholarCross RefCross Ref
  19. Andrei Vlad Sambra, Essam Mansour, Sandro Hawke, Maged Zereba, Nicola Greco, Abdurrahman Ghanem, Dmitri Zagidulin, Ashraf Aboulnaga, and Tim Berners-Lee. 2016. Solid: a platform for decentralized social applications based on linked data. MIT CSAIL & Qatar Computing Research Institute, Tech. Rep. (2016).Google ScholarGoogle Scholar
  20. Bela Stantic. 2017. Provenance-Based Rumor Detection. In Databases Theory and Applications: 28th Australasian Database Conference, ADC 2017, Brisbane, QLD, Australia, September 25-28, 2017, Proceedings, Vol. 10538. Springer, 125.Google ScholarGoogle Scholar
  21. Thorsten Strufe. 2009. Safebook: A privacy-preserving online social network leveraging on real-life trust. IEEE Communications Magazine 95 (2009).Google ScholarGoogle Scholar
  22. Io Taxidou, Sven Lieber, Peter M Fischer, Tom De Nies, and Ruben Verborgh. 2018. Web-scale provenance reconstruction of implicit information diffusion on social media. Distributed and Parallel Databases 36, 1 (2018), 47--79.Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Tuan-Dat Trinh, Peb R Aryan, Ba-Lam Do, Fajar J Ekaputra, Elmar Kiesling, Andreas Rauber, Peter Wetz, and A Min Tjoa. 2017. Linked data processing provenance: towards transparent and reusable linked data integration. In Proceedings of the International Conference on Web Intelligence. ACM, 88--96.Google ScholarGoogle ScholarDigital LibraryDigital Library

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          cover image ACM Conferences
          WebMedia '21: Proceedings of the Brazilian Symposium on Multimedia and the Web
          November 2021
          271 pages
          ISBN:9781450386098
          DOI:10.1145/3470482

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          Publication History

          • Published: 5 November 2021

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          WebMedia '21 Paper Acceptance Rate24of75submissions,32%Overall Acceptance Rate270of873submissions,31%
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