Provenance description of metadata application profiles for long-term maintenance of metadata schemas

Abstract

Purpose Provenance information is crucial for consistent maintenance of metadata schemas over time. The purpose of this paper is to propose a provenance model named DSP-PROV to keep track of structural changes of metadata schemas. Design/methodology/approach The DSP-PROV model is developed through applying the general provenance description standard PROV of the World Wide Web Consortium to the Dublin Core Application Profile. Metadata Application Profile of Digital Public Library of America is selected as a case study to apply the DSP-PROV model. Finally, this paper evaluates the proposed model by comparison between formal provenance description in DSP-PROV and semi-formal change log description in English. Findings Formal provenance description in the DSP-PROV model has advantages over semi-formal provenance description in English to keep metadata schemas consistent over time. Research limitations/implications The DSP-PROV model is applicable to keep track of the structural changes of metadata schema over time. Provenance description of other features of metadata schema such as vocabulary and encoding syntax are not covered. Originality/value This study proposes a simple model for provenance description of structural features of metadata schemas based on a few standards widely accepted on the Web and shows the advantage of the proposed model to conventional semi-formal provenance description.

Metadata Standard for Continuous Preservation, Discovery, and Reuse of Research Data in Repositories by Higher Education Institutions: A Systematic Review

“…Additionally, the Dublin Core metadata element set is made up of 15 elements (title, creator, subject, description, publishers, contributors, date, type format, identifier, source, language, relation, coverage, and right) and addresses the various elements obtained among metadata types (descriptive, administrative, and technical), which are also needed to identify digitised resources and data [17,18,32]. In general, metadata types are critical to establishing an accurate understanding of the nature of resource items of: what, why, where, and how; and (c) The data's organisational structure, including the chapters and articles that make up the data [18,19].…”

“…Metadata also guarantee the preservation of a digital resource/object, for example, archived sites and through specific metadata standards, such as Preservation Metadata Implementation Strategies (PREMIS) and Metadata Encoding and Transmission Standard (METS) [6,19]. Other metadata standards include Dublin Core, Encoded Archival Description (EAD), Visual Resources Association (VRA) Core, Categories for the Description of Works of Art (CDWA), and Machine-Readable Cataloging (MARC) and Metadata Object Description Schema (MODS) [6,19,20]. The Resource Description Access (RDA) metadata standard catalogue is a collaborative, open directory of metadata standards applicable to research data [21].…”

Privacy Rating of Mobile Applications Based on Crowdsourcing and Machine Learning

“…4) It is recommended that the cloud server SP calculate the control parameters and send it to the recommendation requesting user k together with the output data and time stamp t in the algorithm (Hudson et al, 2016). The recommendation request user k receives the relevant data returned by the recommendation cloud server SP on the client, and decrypts the encrypted values obtained in the algorithm, namely X and Y, using their own homomorphic encryption private keys, and the decryption results are recorded as P and Q (Li & Sugimoto, 2018;Piao et al, 2016). For each application i to be recommended in the decryption set, the client calculates the recommendation value for the application i for the recommendation request user k obtained within the time window t according to formula ( 7):…”

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“…Na percepção da preservação digital, a proveniência é importante para registrar informações dos responsáveis pela criação, custódia, alteração, curadoria e administração de um recurso digital (Arakaki, 2019, Arakaki, Alves e Santos, 2019. Li e Sugimoto (2014, 2017e 2018 apresentaram diversas discussões e relações entre a proveniência e a preservação digital. Haynes (2018, p. 134) complementa que "fornecer informações de proveniência pode ajudar a demonstrar que um registro não foi adulterado e que a evidência que ele apresenta é, portanto, confiável".…”