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Enhancing Open Government Data With Data Provenance

Published: 10 January 2020 Publication History

Abstract

The Brazilian Government has adhered to the Linked Open Government Data publication policy. Thus, promoting a more transparent and open administration, allowing greater participation of society, the strengthening of democracy, and combating corruption. All of these matters can be affected by how open data is published. Beyond the data itself, data provenance allows aggregate metadata such as when, how, and why the data were created and published. Given this scenario, we consider that the combination of data and its provenance enriches the traceability of the data exposing the methods and agents involved in its creation. This paper presents a technological solution in the context of Linked Open Government Data to enhance the public open government data publishing. It is delivered employing an information architecture that can provide the data provenance of public open government data using the PROV-DM and a graph database. In addition, we also present an implementation of the proposed information architecture for a public open linked data as a case study.

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cover image ACM Other conferences
MEDES '19: Proceedings of the 11th International Conference on Management of Digital EcoSystems
November 2019
350 pages
ISBN:9781450362382
DOI:10.1145/3297662
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 10 January 2020

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Author Tags

  1. Linked Open Data
  2. Open Government Data
  3. PROV-DM
  4. Provenance

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MEDES '19

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MEDES '19 Paper Acceptance Rate 41 of 102 submissions, 40%;
Overall Acceptance Rate 267 of 682 submissions, 39%

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