CubeViz: The RDF DataCube Browser.

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CubeViz is a facetted browser for statistical data utilizing the RDF Data Cube vocabulary which is the state-of-the-art in representing statistical data in RDF. This vocabulary is compatible with SDMX and increasingly being adopted. Based on the vocabulary and the encoded Data Cube, CubeViz is generating a facetted browsing widget that can be used to filter interactively observations to be visualized in charts. Based on the selected structure, CubeViz offer beneficiary chart types and options which can be selected by users.

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In order to hide the complexity of the RDF Data Cube vocabulary from users and to facilitate the browsing and exploration of DataCubes we developed the RDF DataCube browser CubeViz. CubeViz can be divided into two parts, both developed as an extension of OntoWiki:

  1. Faceted data selection component, which queries the structural part of a selected RDF graph containing DataCube resources.
  2. Chart visualization component, which queries observations (selected by the faceted selection component) and visualize them with suitable charts.

CubeViz renders facets according to the DataCube vocabulary to select data on the first component, using SPARQL as the query language. Currently, the following facets are available:

  1. Selection of a DataCube DataSet
  2. Selection of a DataCube Slice
  3. Selection of a specific measure and attribute (unit) property encoded in the respective DataCube dataset.
  4. Selection of a set of dimension elements that are part of the dimensions encoded in the respective DataCube data set

Project Team


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AKSW Colloquium, 22-06-2015, Concept Expansion Using Web Tables, Mining entities from the Web, Linked Data Stack ( 2015-06-22T12:11:56+02:00 by Mohamed Sherif)

2015-06-22T12:11:56+02:00 by Mohamed Sherif

Concept Expansion Using Web Tables by Chi Wang, Kaushik Chakrabarti, Yeye He,Kris Ganjam, Zhimin Chen, Philip A. Bernstein (WWW’2015), presented by Ivan Ermilov: Abstract. Read more about "AKSW Colloquium, 22-06-2015, Concept Expansion Using Web Tables, Mining entities from the Web, Linked Data Stack"

AKSW Colloquium, 15-06-2015, Caching for Link Discovery ( 2015-06-12T23:33:25+02:00 TommasoSoru)

2015-06-12T23:33:25+02:00 TommasoSoru

Using Caching for Local Link Discovery on Large Data Sets [PDF] by Mofeed Hassan Engineering the Data Web in the Big Data era demands the development of time- and space-efficient solutions for covering the lifecycle of Linked Data. Read more about "AKSW Colloquium, 15-06-2015, Caching for Link Discovery"

AKSW Colloquium, 08-06-2015, DBpediaSameAs, Dynamic-LOD ( 2015-06-05T02:00:35+02:00 by Dr. Amrapali Zaveri)

2015-06-05T02:00:35+02:00 by Dr. Amrapali Zaveri

DBpediaSameAs: An approach to tackling heterogeneity in DBpedia identifiers by Andre Valdestilhas This work provides an approach to tackle heterogeneity about a problem where several transient owl:sameAs redundant occurrences were found in DBpedia identifiers during searching for owl:sameAs occurrences that … Continue reading → Read more about "AKSW Colloquium, 08-06-2015, DBpediaSameAs, Dynamic-LOD"

Smart Data Web project kick-off ( 2015-06-01T13:55:29+02:00 MartinBruemmer)

2015-06-01T13:55:29+02:00 MartinBruemmer

Smart Data Web, a new BMWi funded project kicked-off in Berlin. Central goal of Smart Data Web is leveraging state-of-the-art data extraction and enrichment technologies as well as Linked Data to create value-added systems for German industry. Read more about "Smart Data Web project kick-off"

AKSW Colloquium, 01-06-2015, MEX – Publishing ML Experiment Results, Scaling DL-Learner – Status and Plans ( 2015-05-31T22:23:34+02:00 by Simon Bin)

2015-05-31T22:23:34+02:00 by Simon Bin

MEX – Publishing ML Experiment Results by Diego Esteves Over the decades many machine learning experiments have been published, collaborating with the scientific community progress. Read more about "AKSW Colloquium, 01-06-2015, MEX – Publishing ML Experiment Results, Scaling DL-Learner – Status and Plans"