Discovery and Visual Analysis of Linked Data for Humans

Sabol, Vedran; Tschinkel, Gerwald; Veas, Eduardo; Hoefler, Patrick; Mutlu, Belgin

doi:10.1007/978-3-319-11964-9_20

Cited by 15 publications

(12 citation statements)

References 13 publications

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“…The dashboards of Jern [25] and Hienert [23] used SDMX as they were built before 2012. One of the first QB dashboard examples by Sabol et al [40] extends earlier work [36,24] and allows brushing over multiple coordinated visualizations. Sabol's paper analyzes two scenarios (search and analysis over LOD, analysis of scientific publications), describes the underlying workflow, and the resulting visualizations implemented in the extensions of the Visualization Wizard tool.…”

Section: Statistical Linked Data Visualizationsupporting

confidence: 56%

“…Once the data is indexed, any query has to lead to at least one visualization. As opposed to approaches that focus on creating a single chart [40], the goal was to generate a set of linked visualizations. This simplifies the process for first-time users, who do not need to choose a particular representation of the data representation, but can look at the data from different perspectives.…”

Section: Architecture and Workflowmentioning

confidence: 99%

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Visualizing statistical linked knowledge for decision support

Brașoveanu

Sabou

Scharl

et al. 2016

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Abstract. In a global and interconnected economy, decision makers often need to consider information from various domains. A tourism destination manager, for example, has to correlate tourist behavior with financial and environmental indicators to allocate funds for strategic long-term investments. Statistical data underpins a broad range of such cross-domain decision tasks. A variety of statistical datasets are available as Linked Open Data, often incorporated into visual analytics solutions to support decision making. What are the principles, architectures, workflows and implementation design patterns that should be followed for building such visual cross-domain decision support systems. This article introduces a methodology to integrate and visualize cross-domain statistical data sources by applying selected RDF Data Cube (QB) principles. A visual dashboard built according to this methodology is presented and evaluated in the context of two use cases in the tourism and telecommunications domains.

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Section: Statistical Linked Data Visualizationsupporting

confidence: 56%

Section: Architecture and Workflowmentioning

confidence: 99%

Visualizing statistical linked knowledge for decision support

Brașoveanu

Sabou

Scharl

et al. 2016

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“…Salient characteristics that distinguish our work from these related efforts are as follows: (i) Whereas many existing approaches [18,2,3] typically aim to integrate data from sources that alreay use consistent terminology, we provide an environment that supports statistical integration of heterogeneous data sources. (ii) We introduce mechanisms to automatically interconnect statistical data sets rather than manually providing co-reference information [12,14,19,20] or data set description [17].…”

Section: Example Mashupsmentioning

confidence: 99%

“…To this end, a visualization wizard [18] for accessing, filtering, and visualizing statistical data was developed. Simultaneous visualizations of many data sets are made possible based on similar component and values of respective data sets.…”

Section: Example Mashupsmentioning

confidence: 99%

Toward a statistical data integration environment

Trinh

Aryan

et al. 2015

Proceedings of the 11th International Conference on Semantic Systems

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In most government and business organizations alike, statistical data provides the foundation for strategic planning and for the management of operations. In this context, the use of increasingly abundant statistical data available on the web creates new opportunities for interesting applications and facilitates more informed decision-making. For the majority of end users, however, viable means to explore statistical data sets available on the web are still scarce. Gathering and relating statistical data from multiple sources is hence typically a tedious manual process that requires significant technical expertise. Data that is being published with associated semantics, using standards such as the W3C RDF Data Cube Vocabulary, lays the foundation to overcome such limitations. In this paper, we develop a semantic metadata repository that describes each statistical data set and develop mechanisms for the interconnection of data sets based on their metadata. Finally, we support users in exploring data sets through interactive mashups that facilitate data integration, comparisons, and visualization.

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“…There are also other resources reporting that learning LD technologies is a problem. For example, [121] describes a toolset for accessing LD by users without the knowledge of LD technologies. A recent blog post "Why should you learn SPARQL?…”

Section: Introductionmentioning

confidence: 99%

Survey of tools for Linked Data consumption

Klímek

Škoda

Nečaský

2019

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There is lots of data published as Linked (Open) Data (LOD/LD). At the same time, there is also a multitude of tools for publication of LD. However, potential LD consumers still have difficulty discovering, accessing and exploiting LD. This is because compared to consumption of traditional data formats such as XML and CSV files, there is a distinct lack of tools for consumption of LD. The promoters of LD use the well-known 5-star Open Data deployment scheme to suggest that consumption of LD is a better experience once the consumer knows RDF and related technologies. This suggestion, however, falls short when the consumers search for an appropriate tooling support for LD consumption. In this paper we define a LD consumption process. Based on this process and current literature, we define a set of 34 requirements a hypothetical Linked Data Consumption Platform (LDCP) should ideally fulfill. We cover those requirements with a set of 94 evaluation criteria. We survey 110 tools identified as potential candidates for an LDCP, eliminating them in 3 rounds until 16 candidates for remain. We evaluate the 16 candidates using our 94 criteria. Based on this evaluation we show which parts of the LD consumption process are covered by the 16 candidates. Finally, we identify 8 tools which satisfy our requirements on being a LDCP. We also show that there are important LD consumption steps which are not sufficiently covered by existing tools. The authors of LDCP implementations may use this survey to decide about directions of future development of their tools. LD experts may use it to see the level of support of the state of the art technologies in existing tools. Non-LD experts may use it to choose a tool which supports their LD processing needs without requiring them to have expert knowledge of the technologies. The paper can also be used as an introductory text to LD consumption.

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Discovery and Visual Analysis of Linked Data for Humans

Cited by 15 publications

References 13 publications

Visualizing statistical linked knowledge for decision support

Visualizing statistical linked knowledge for decision support

Toward a statistical data integration environment

Survey of tools for Linked Data consumption

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