Open Data Quality

Researches and publications about quality of open data

Tag: data quality (Page 1 of 3)

Enhancing the Interactive Visualisation of a Data Preparation Tool from in-Memory Fitting to Big Data Sets

This study presents the challenges faced and the solutions adopted while evolving the web-based graphical user interface (GUI) of a tabular data preparation tool from in-memory fitting to Big Data sets. Traditional standalone processing and rendering solutions are no longer usable in a Big Data context.

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Analyzing OpenStreetMap Contributions at Scale: Introducing OSM-Interactions Tilesets

OSM-interaction tilesets are vector tiles containing GeoJSON features that represent interactions between mappers (contributors) and objects in OpenStreetMap (OSM). Interactions are abstractions of edits to OSM elements called nodes, ways, and relations. Example interactions are contributors “adjusting the corners of a building” or “re-aligning a road” while the edit to the database is recorded as a “modification to the coordinates of a node.”

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Semantic Data Integration and Quality Assurance of Thematic Maps in the German Federal Agency for Cartography and Geodesy

In this paper authors present a new concept of geospatial quality assurance that is currently planned to be implemented in the German Federal Agency of Cartography and Geodesy. Linked open data is being enriched with Semantic Web data in order to create thematic maps relevant to the population.

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Evaluating the Quantity of Incident-Related Information in an Open Cyber Security Dataset

Data-driven security has become essential in many organisations in their attempt to tackle Cyber security incidents. However, whilst the dominant approach to data-driven security remains through the mining of private and internal data, there is an increasing trend towards more open data through the sharing of Cyber security information and experience over public and community platforms. However, some questions remain over the quality and quantity of such open data.

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A New Tool for Automated Quality Control of Environmental Time Series (AutoQC4Env) in Open Web Services

Athors report on the development of a new software tool (AutoQC4Env) for automated quality control (QC) of environmental time series data. Novel features of this tool include a flexible Python software architecture, which makes it easy for users to configure the sequence of tests as well as their statistical parameters, and a statistical concept to assign each value a probability of being a valid data point.

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Special Issue “Quality of Open Data”

The 2nd International Workshop on Quality of Open Data (QOD 2019) will be held in June 2019 in Seville in conjunction with the 22nd International Conference on Business Information Systems. The goal of the workshop is to bring together different communities working on quality in Wikipedia, DBpedia, Wikidata, OpenStreetMap, Wikimapia, and other open knowledge bases. The workshop calls for sharing research experiences and knowledge related to quality assessment in open data.

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Access Control and Quality Attributes of Open Data: Applications and Techniques

Open Datasets provide one of the most popular ways to acquire insight and information about individuals, organizations and multiple streams of knowledge. Exploring Open Datasets by applying comprehensive and rigorous techniques for data processing can provide the ground for innovation and value for everyone if the data are handled in a legal and controlled way. In this study, authors propose an argumentation and abductive reasoning approach for data processing which is based on the data quality background.

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Situation-Dependent Data Quality Analysis for Geospatial Data Using Semantic Technologies

In this paper authors presents a new way to evaluate geospatial data quality using Semantic technologies. In contrast to non-semantic approaches to evaluate data quality, Semantic technologies allow them to model situations in which geospatial data may be used and to apply costumized geospatial data quality models using reasoning algorithms on a broad scale.

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Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications

Today׳s supply chain professionals are inundated with data, motivating new ways of thinking about how data are produced, organized, and analyzed. This has provided an impetus for organizations to adopt and perfect data analytic functions (e.g. data science, predictive analytics, and big data) in order to enhance supply chain processes and, ultimately, performance.

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Big Data Quality: A Roadmap for Open Data

Open Data (OD) is one of the most discussed issue of Big Data which raised the joint interest of public institutions, citizens and private companies since 2009. However, the massive amount of freely available data has not yet brought the expected effects: as of today, there is no application that has fully exploited the potential provided by large and distributed information sources in a non-trivial way, nor any service has substantially changed for the better the lives of people.

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