NTTS 2015, Brussels, 10-12 March 2015

ICT Tools for statistical linked open data: the OpenCube toolkit

E. Tambouris, E. Kalampokis, K. Tarabanis

University of Macedonia and ITI-CERTH, Greece {tambouris, ekal, kat}@uom.gr

Open Statistical Data are very important for the EU

Users frequently want to blend & combine statistical data from multiple sources

But, these data usually resides in files and databases (data silos) that are hard to combine

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Problem definition

Linked Data (LD) technology has the potential to enable combining and performing analytics on top of disparate and previously isolated statistical data

However, relevant tools are few, scattered and un-tested under real-life conditions

Potential of using LD in statistical data analysis unexploited

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The OpenCube project

OpenCube is a 2-year project funded by the EU within FP7

The project aims to develop and test processes and tools for managing statistical

linked open data.

The results will:

Facilitate data publishers to create linked data cubes from legacy formats

Empower data users to browse, visualise, link, expand and analyse data cubes.

Enable analysis not possible before (merging data cubes at a Web scale)

We propose a lifecycle for statistical LD

The lifecycle is divided into two phases: publish and reuse (or consume)

The lifecycle prescribes the steps that raw data cubes* should go through in order to create value.

OpenCube also develops tools to support the whole lifecycle of linked statistical data.

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Linked Statistical Data Lifecycle

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* We assume statistical data is organized as data cubes, where each cell contains a measure described based on a number of dimensions.

Publishing components TARQL extension

D2RQ /R2RML-QB extension

JSON-stat

Grafter

Consuming components OpenCube Browser

OpenCube MapView

R Analysis Chart

Linking components

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OpenCube Toolkit

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Developed using Information Workbench open source as underlying linked data management platform

License scheme OpenCube components are

provided under open source licenses

Check http://opencube-toolkit.eu

But, commercial solutions are also offered by consortium members

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Publishing Components

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Consume: OpenCube browser

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Summarize observations

across a dimension

(dimension reduction)

Change the axes

of the table

Change the

language

Change the fixed

values

It enables the exploration of an RDF data cube by presenting a two-dimensional slice of the cube as a table.

The slice is created by setting a fixed values for each dimension that is not presented in the table.

Visualization of RDF data cubes on a map.

It supports: Markers

Bubble

Choropleth maps

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Consume: OpenCube MapView

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Visualisation of analysis results (charts & tables)

Reuse of analysis results: preserving R output as linked data

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Consume: Integration with R

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Consume: Other Visualizations

Analytics and Reporting Visualization and Exploration

Stock chart

Enables Performing analytics on top of combined data cubes

Steps: 1. Select a data cube

2. Discover cubes on the Web of Linked Data having compatible structure; i.e. cubes with dimensions, measures etc. that can expand the initial cube

3. Create expanded views of the initial cube

4. Consume the new cube(s)

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Linking Statistical Data

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Example: Start with an initial cube

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Example: Discover & Select compatible cubes

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Example: Browse an expanded view of the initial cube

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Open Statistical data are rapidly increasing due to Open Data policies

Linked Data technologies can provide web-scale linking and analysis of statistical data

OpenCube project develops processes and tools for statistical data management

These can be divided into: Tools for producing linked open statistical data

Tools for linking (expanding) open statistical data

Tools for consuming linked open statistical data

Practical use of the tools is possible in the NTTS 2015 satellite workshop session 21B on Linked Statistics (today 18:00-20:00)

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Conclusions

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For more information

http://opencube-project.eu

http://opencube-toolkit.eu

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Questions?

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OpenCube consortium