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R. Pandey and M. Pande
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. Foster, I., Kesselman, C., Nick, J.M., Tuecke, S.: Grid services for distributed system
integration. Computer 35, 37–46 (2002)
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)
6. w3.org/TR/2020/CR-owl-time-20200326/, Time Ontology in OWL W3C Candidate Recommendation 26 Mar 2020
7. PROV-N: The Provenance Notation [Online]. Available https://www.w3.org/TR/prov-n/ (2013)
8. Tan, W.C.: Provenance in databases: past, current, and future. IEEE Data Eng. Bull. 30, 3–12
(2007)
9. Bose, R., Frew, J.: Lineage retrieval for scientific data processing: a survey. ACM Comput.
Surveys (CSUR) 37, 1–28 (2005)
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)
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)
12. Lakshmanan, G.T., Curbera, F.: Provenance in web applications. IEEE Internet Comput. 15(1),
17–21 (2011)
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)
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. 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)
16. Missier, P., Belhajjame, K., Cheney, J.: The W3C PROV family of specifications for modelling
provenance metadata. In: EDBT’13 (2013)
17. Groth, P., Moreau, L.: PROV-Overview. An Overview of the PROV Family of Documents
(2013)
18. Simmhan, Y.L., Plale, B., Gannon, D.: A survey of data provenance in e-science. ACM
SIGMOD Record 34, 31–36 (2005)
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)
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)
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)
22. Constraints of the PROV Data Model [Online]. Available https://www.w3.org/TR/2013/RECprov-constraints-20130430/ (2013)
23. PROV-DM: The PROV Data Model [Online]. Available https://www.w3.org/TR/prov-dm/
(2013)
24. https://www.provenir.com/decision-engine-software/decisioning-platform/
25. https://dvcs.w3.org/hg/prov/raw-file/default/ontology/working-dir/pml-3/Overview.html
26. The W3C Working Charter [Online]. Available https://www.w3.org/2011/prov/wiki/Interoper
ability
27. OWL Web Ontology Language XML Presentation Syntax (2003)
R. Pandey and M. Pande
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. Foster, I., Kesselman, C., Nick, J.M., Tuecke, S.: Grid services for distributed system
integration. Computer 35, 37–46 (2002)
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)
6. w3.org/TR/2020/CR-owl-time-20200326/, Time Ontology in OWL W3C Candidate Recommendation 26 Mar 2020
7. PROV-N: The Provenance Notation [Online]. Available https://www.w3.org/TR/prov-n/ (2013)
8. Tan, W.C.: Provenance in databases: past, current, and future. IEEE Data Eng. Bull. 30, 3–12
(2007)
9. Bose, R., Frew, J.: Lineage retrieval for scientific data processing: a survey. ACM Comput.
Surveys (CSUR) 37, 1–28 (2005)
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)
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)
12. Lakshmanan, G.T., Curbera, F.: Provenance in web applications. IEEE Internet Comput. 15(1),
17–21 (2011)
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)
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. 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)
16. Missier, P., Belhajjame, K., Cheney, J.: The W3C PROV family of specifications for modelling
provenance metadata. In: EDBT’13 (2013)
17. Groth, P., Moreau, L.: PROV-Overview. An Overview of the PROV Family of Documents
(2013)
18. Simmhan, Y.L., Plale, B., Gannon, D.: A survey of data provenance in e-science. ACM
SIGMOD Record 34, 31–36 (2005)
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)
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)
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)
22. Constraints of the PROV Data Model [Online]. Available https://www.w3.org/TR/2013/RECprov-constraints-20130430/ (2013)
23. PROV-DM: The PROV Data Model [Online]. Available https://www.w3.org/TR/prov-dm/
(2013)
24. https://www.provenir.com/decision-engine-software/decisioning-platform/
25. https://dvcs.w3.org/hg/prov/raw-file/default/ontology/working-dir/pml-3/Overview.html
26. The W3C Working Charter [Online]. Available https://www.w3.org/2011/prov/wiki/Interoper
ability
27. OWL Web Ontology Language XML Presentation Syntax (2003)