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

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News

SML-Bench 0.2 Released ( 2017-05-11T13:01:45+02:00 by Patrick Westphal)

2017-05-11T13:01:45+02:00 by Patrick Westphal

Dear all, we are happy to announce the 0.2 release of SML-Bench, our Structured Machine Learning benchmark framework. SML-Bench provides full benchmarking scenarios for inductive supervised machine learning covering different knowledge representation languages like OWL and Prolog. Read more about "SML-Bench 0.2 Released"

AKSW Colloquium, 08.05.2017, Scalable RDF Graph Pattern Matching ( 2017-05-08T09:42:49+02:00 by Lorenz Bühmann)

2017-05-08T09:42:49+02:00 by Lorenz Bühmann

At the AKSW Colloquium, on Monday 8th of May 2017, 3 PM, Lorenz Bühmann will discuss a paper titled “Type-based Semantic Optimization for Scalable RDF Graph Pattern Matching” of Kim et al. Read more about "AKSW Colloquium, 08.05.2017, Scalable RDF Graph Pattern Matching"

ESWC 2017 accepted two Demo Papers by AKSW members ( 2017-04-19T10:19:43+02:00 Christopher Schulz)

2017-04-19T10:19:43+02:00 Christopher Schulz

Hello Community! The 14th ESWC, which takes place from May 28th to June 1st 2017 in Portoroz, Slovenia, accepted two demos to be presented at the conference. Read more about them in the following:                                                                         1. Read more about "ESWC 2017 accepted two Demo Papers by AKSW members"

AKSW Colloquium, 10.04.2017, GeoSPARQL on geospatial databases ( 2017-04-07T10:43:55+02:00 by Dr. Matthias Wauer)

2017-04-07T10:43:55+02:00 by Dr. Matthias Wauer

At the AKSW Colloquium, on Monday 10th of April 2017, 3 PM, Matthias Wauer will discuss a paper titled “Ontop of Geospatial Databases“. Read more about "AKSW Colloquium, 10.04.2017, GeoSPARQL on geospatial databases"

AKSW Colloquium, 03.04.2017, RDF Rule Mining ( 2017-03-31T13:39:28+02:00 TommasoSoru)

2017-03-31T13:39:28+02:00 TommasoSoru

At the AKSW Colloquium, on Monday 3rd of April 2017, 3 PM, Tommaso Soru will present the state of his ongoing research titled “Efficient Rule Mining on RDF Data”, where he will introduce Horn Concerto, a novel scalable SPARQL-based approach … Continue reading → Read more about "AKSW Colloquium, 03.04.2017, RDF Rule Mining"