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

Publications

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News

AKSW Colloquium, 28.11.2016, NED using PBOH + Large-Scale Learning of Relation-Extraction Rules. ( 2016-11-26T12:30:29+01:00 by Diego Moussallem)

2016-11-26T12:30:29+01:00 by Diego Moussallem

In the upcoming Colloquium, November the 28th at 3 PM, two papers will be presented: Probabilistic Bag-Of-Hyperlinks Model for Entity Linking Diego Moussallem will discuss the paper “Probabilistic Bag-Of-Hyperlinks Model for Entity Linking” by Octavian-Eugen Ganea et. al. Read more about "AKSW Colloquium, 28.11.2016, NED using PBOH + Large-Scale Learning of Relation-Extraction Rules."

Accepted paper in AAAI 2017 ( 2016-11-14T14:48:46+01:00 by Mohamed Sherif)

2016-11-14T14:48:46+01:00 by Mohamed Sherif

Hello Community! Read more about "Accepted paper in AAAI 2017"

AKSW Colloquium, 17.10.2016, Version Control for RDF Triple Stores + NEED4Tweet ( 2016-10-17T09:55:50+02:00 by Marvin Frommhold)

2016-10-17T09:55:50+02:00 by Marvin Frommhold

In the upcoming Colloquium, October the 17th at 3 PM, two papers will be presented: Version Control for RDF Triple Stores Marvin Frommhold will discuss the paper “Version Control for RDF Triple Stores” by Steve Cassidy and James Ballantine which forms the foundation … Continue reading → Read more about "AKSW Colloquium, 17.10.2016, Version Control for RDF Triple Stores + NEED4Tweet"

LIMES 1.0.0 Released ( 2016-10-14T11:38:31+02:00 by Kleanthi Georgala)

2016-10-14T11:38:31+02:00 by Kleanthi Georgala

Dear all, the LIMES Dev team is happy to announce LIMES 1.0.0. LIMES, the Link Discovery Framework for Metric Spaces, is a link discovery framework for the Web of Data. Read more about "LIMES 1.0.0 Released"

DL-Learner 1.3 (Supervised Structured Machine Learning Framework) Released ( 2016-10-11T21:41:00+02:00 by Dr. Jens Lehmann)

2016-10-11T21:41:00+02:00 by Dr. Jens Lehmann

Dear all, the Smart Data Analytics group at AKSW is happy to announce DL-Learner 1.3. DL-Learner is a framework containing algorithms for supervised machine learning in RDF and OWL. Read more about "DL-Learner 1.3 (Supervised Structured Machine Learning Framework) Released"