Explorations in provenance in the information sciences

Gryk, Michael Robert

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https://hdl.handle.net/2142/127214 Copy

Description

Title

Explorations in provenance in the information sciences

Author(s)

Gryk, Michael Robert

Issue Date

2024-12-01

Director of Research (if dissertation) or Advisor (if thesis)

Bertram Ludaescher, Bertram

Doctoral Committee Chair(s)

Bertram Ludaescher, Bertram

Committee Member(s)

• Bettivia, Rhiannon
• Downie, J. Stephen
• Twidale, Michael

Department of Study

Information Sciences

Discipline

Library & Information Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Provenance
• FAIR Principles
• Curation
• Workflows
• Information Modeling

Abstract

Provenance is important throughout Library and Information Science and is particularly important for the information infrastructures which support the computational aspects of the natural sciences. This is highlighted by the prominence of provenance as a plank in the FAIR principles for data stewardship (principle R1.2). While traditionally focused on the history/lineage of physical objects, provenance is now commonly accepted to apply to digital objects such as the results of computation as well as to the recipes for computing; in the case of recipes this prospective provenance is critical for reproducibility. This dissertation begins with background in provenance pertaining to data curation and computational reproducibility. The second part describes attempts to “FAIRify” the reporting and execution of workflows within a domain of natural science for better data stewardship to support data reusability. The next chapters argue that there remains a gap in our ability to fully document provenance as there are more story-telling tenses than just the past (retrospective) and future (prospective). There is also the subjunctive (conditional) and perhaps many others. Supporting new flavors of provenance requires new modeling constructs. The thesis concludes with novel information modeling techniques which exploit reification of sub-class relationships suitable for modeling these many sub-classes of provenance, as well as other domains.

Graduation Semester

2024-12

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/127214

Copyright and License Information

© 2024 Michael Robert Gryk

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