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, 15.02.2016, Mandolin + X-Feasible ( 2016-02-13T20:48:58+01:00 TommasoSoru)

2016-02-13T20:48:58+01:00 TommasoSoru

On the 15th of February at 3 PM, Tommaso Soru will present his ongoing research titled “Mandolin: Markov Logic Networks for Discovering Links”. Read more about "AKSW Colloquium, 15.02.2016, Mandolin + X-Feasible"

DL-Learner 1.2 (Supervised Structured Machine Learning Framework) Released ( 2016-02-09T16:19:55+01:00 by Patrick Westphal)

2016-02-09T16:19:55+01:00 by Patrick Westphal

Dear all, we are happy to announce DL-Learner 1.2. DL-Learner is a framework containing algorithms for supervised machine learning in RDF and OWL. Read more about "DL-Learner 1.2 (Supervised Structured Machine Learning Framework) Released"

AKSW Colloquium, 01.02.2016, Co-evolution of RDF Datasets ( 2016-02-01T15:53:23+01:00 by Natanael Arndt)

2016-02-01T15:53:23+01:00 by Natanael Arndt

At the todays colloquium, Natanael Arndt will discuss the the paper “Co-evolution of RDF Dataset” by Sidra Faisal, Kemele M. Endris, Saeedeh Shekarpour and Sören Auer (2016, available on arXiv) Link: Read more about "AKSW Colloquium, 01.02.2016, Co-evolution of RDF Datasets"

Holographic Embeddings of Knowledge Graphs ( 2016-02-01T14:32:03+01:00 by Johannes Frey)

2016-02-01T14:32:03+01:00 by Johannes Frey

During the upcoming colloquium, Nilesh Chakraborty will give a short introduction on factorising RDF tensors and present a paper on “Holographic Embeddings of Knowledge Graphs”: Holographic Embeddings of Knowledge Graphs Authors: Maximilian Nickel, Lorenzo Rosasco, Tomaso Poggio Abstract: Learning embeddings … Continue reading → Read more about "Holographic Embeddings of Knowledge Graphs"

AKSW Colloquium, 25.01.2016, LargeRDFBench and Introduction To The Docker Ecosystem ( 2016-01-25T14:39:49+01:00 by Ivan Ermilov)

2016-01-25T14:39:49+01:00 by Ivan Ermilov

On the upcoming colloquium, Muhammad Saleem will present his paper “LargeRDFBench: A Billion Triples Benchmark for SPARQL Endpoint Federation” about the benchmarking of federated SPARQL endpoints. The other talk will be an introduction to the Docker ecosystem by Tim Ermilov. Read more about "AKSW Colloquium, 25.01.2016, LargeRDFBench and Introduction To The Docker Ecosystem"