Quality Assessment and Evolution of Open Data Portals

Umbrich, Jürgen; Neumaier, Sebastian; Polleres, Axel

doi:10.1109/ficloud.2015.82

Cited by 62 publications

(71 citation statements)

References 3 publications

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“…[3,2]). Our study considers some of those dimensions, and particularly the ones introduced in [19], namely:…”

Section: Open Data Quality Dimensionsmentioning

confidence: 99%

“…A set of quality dimensions, based on each of the above categories, have been defined in our previous work [19], as will be discussed in the next section.…”

Section: Open Data Platformsmentioning

confidence: 99%

“…Although a semantic distance could be introduced in order to highlight how "open" a license or format is (e.g., usage of the data is allowed but not the redistribution of modified values), it is very challenging to define such a distance for all licenses and formats. Accordingly, the metric proposed in [19] evaluates whether the specified license (per dataset) is included in the list provided by the Open Definition. This list contains details about 108 different licenses including their typical id, url, title and an assessment whether or not they are considered as "open".…”

Section: Open Data Quality Dimensionsmentioning

confidence: 99%

“…The usage metric, formally detailed in [19], is the degree (%) to which the available meta data keys are used in the datasets of a given portal. We use this measure since we observed that not all portals make all meta data keys available to the data publishers or because keys can be left out if publishers use the CKAN API.…”

Section: Open Data Quality Dimensionsmentioning

confidence: 99%

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Open Data Portal Quality Comparison using AHP

Kubler

Robert

Traon

et al. 2016

Proceedings of the 17th International Digital Government Research Conference on Digital Government Research

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During recent years, more and more Open Data becomes available and used as part of the Open Data movement. However, there are reported issues with the quality of the metadata in data portals and the data itself. This is a serious risk that could disrupt the Open Data project, as well as e-government initiatives since the data quality needs to be managed to guarantee the reliability of e-government to the public. First quality assessment frameworks emerge to evaluate the quality for a given dataset or portal along various dimensions (e.g., information completeness). Nonetheless, a common problem with such frameworks is to provide meaningful ranking mechanisms that are able to integrate several quality dimensions and user preferences (e.g., a portal provider is likely to have different quality preferences than a portal consumer). To address this multi-criteria decision making problem, our research work applies AHP (Analytic Hierarchy Process), which compares 146 active Open Data portals across 44 countries, powered by the CKAN software.

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“…[3,2]). Our study considers some of those dimensions, and particularly the ones introduced in [19], namely:…”

Section: Open Data Quality Dimensionsmentioning

confidence: 99%

“…A set of quality dimensions, based on each of the above categories, have been defined in our previous work [19], as will be discussed in the next section.…”

Section: Open Data Platformsmentioning

confidence: 99%

Section: Open Data Quality Dimensionsmentioning

confidence: 99%

Section: Open Data Quality Dimensionsmentioning

confidence: 99%

See 2 more Smart Citations

Open Data Portal Quality Comparison using AHP

Kubler

Robert

Traon

et al. 2016

Proceedings of the 17th International Digital Government Research Conference on Digital Government Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…in the context of i) smart cities for open data portal quality assessment, e.g. in (Umbrich et al, 2015) whose metadata items are assessed in terms of quality; ii) product lifecycle management, e.g. in (Wellsandt et al, 2015) where authors separate 'fitting' from 'unfitting' information from a manufacturing/design decision process perspective; iii) query processing, e.g.…”

Section: Data Quality Frameworkmentioning

confidence: 99%

Data quality assessment of maintenance reporting procedures

Madhikermi

Kubler

Robert

et al. 2016

Expert Systems with Applications

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Today's largest and fastest growing companies' assets are no longer physical, but rather digital (software, algorithms. . . ). This is all the more true in the manufacturing, and particularly in the maintenance sector where quality of enterprise maintenance services are closely linked to the quality of maintenance data reporting procedures. If quality of the reported data is too low, it can results in wrong decision-making and loss of money. Furthermore, various maintenance experts are involved and directly concerned about the quality of enterprises' daily maintenance data reporting (e.g., maintenance planners, plant managers. . . ), each one having specific needs and responsibilities. To address this Multi-Criteria Decision Making (MCDM) problem, and since data quality is hardly considered in existing expert maintenance systems, this paper develops a Maintenance Reporting Quality Assessment (MRQA) dashboard that enables any company stakeholder to easily -and in real-time -assess/rank company branch offices in terms of maintenance reporting quality. From a theoretical standpoint, AHP is used to integrate various data quality dimensions as well as expert preferences. A use case describes how the proposed MRQA dashboard is being used by a Finnish multinational equipment manufacturer to assess and enhance reporting practices in a specific or a group of branch offices.

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Integrating user feedback with open data quality models

Weber

Yan

2017

Proc. Assoc. Info. Sci. Tech.

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User feedback is critical to improving the quality of open data. However, most open data publishers gather only anecdotal evidence about user experiences. This unstructured and informal commentary is, consequently, difficult to translate into actionable steps towards improving data quality. Drawing on user comments collected from Data.Gov -an open data portal providing access to thousands of datasets published by city, state, and federal government agencies in the USA -we inductively develop a classification of reported data quality issues. This poster presents preliminary findings from applying this coding scheme to all issues that users filed on Data.gov in 2015 and 2016. We suggest that our classification scheme can help open data publishers collect structured, actionable information to improve data quality.

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Quality Assessment and Evolution of Open Data Portals

Cited by 62 publications

References 3 publications

Open Data Portal Quality Comparison using AHP

Open Data Portal Quality Comparison using AHP

Data quality assessment of maintenance reporting procedures

Integrating user feedback with open data quality models

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