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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
2
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
12 March 2015 NTTS 2015, Brussels, 10-12 March 2015
12 March 2015 NTTS 2015, Brussels, 10-12 March 2015 3
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.
12 March 2015 NTTS 2015, Brussels, 10-12 March 2015
Linked Statistical Data Lifecycle
4
* 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
5
OpenCube Toolkit
12 March 2015 NTTS 2015, Brussels, 10-12 March 2015
 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
6
Publishing Components
12 March 2015 NTTS 2015, Brussels, 10-12 March 2015
7
Consume: OpenCube browser
12 March 2015 NTTS 2015, Brussels, 10-12 March 2015
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
8
Consume: OpenCube MapView
12 March 2015 NTTS 2015, Brussels, 10-12 March 2015
 Visualisation of analysis results (charts & tables)
 Reuse of analysis results: preserving R output as linked data
12 March 2015 NTTS 2015, Brussels, 10-12 March 2015 9
Consume: Integration with R
12 March 2015 NTTS 2015, Brussels, 10-12 March 2015 10
Consume: Other Visualizations
Analytics and ReportingVisualization 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)
11
Linking Statistical Data
12 March 2015 NTTS 2015, Brussels, 10-12 March 2015
12
Example: Start with an initial cube
12 March 2015 NTTS 2015, Brussels, 10-12 March 2015
13
Example: Discover & Select compatible cubes
12 March 2015 NTTS 2015, Brussels, 10-12 March 2015
14
Example: Browse an expanded view of the initial cube
12 March 2015 NTTS 2015, Brussels, 10-12 March 2015
 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)
15
Conclusions
12 March 2015 NTTS 2015, Brussels, 10-12 March 2015
 For more information
 http://opencube-project.eu
 http://opencube-toolkit.eu
16
Questions?
12 March 2015 NTTS 2015, Brussels, 10-12 March 2015
 OpenCube consortium

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FP7 OpenCube project presentation at NTTS 2015 conference

  • 1. 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
  • 2.  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 2 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 12 March 2015 NTTS 2015, Brussels, 10-12 March 2015
  • 3. 12 March 2015 NTTS 2015, Brussels, 10-12 March 2015 3 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)
  • 4.  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. 12 March 2015 NTTS 2015, Brussels, 10-12 March 2015 Linked Statistical Data Lifecycle 4 * We assume statistical data is organized as data cubes, where each cell contains a measure described based on a number of dimensions.
  • 5.  Publishing components  TARQL extension  D2RQ /R2RML-QB extension  JSON-stat  Grafter  Consuming components  OpenCube Browser  OpenCube MapView  R Analysis Chart  Linking components 5 OpenCube Toolkit 12 March 2015 NTTS 2015, Brussels, 10-12 March 2015  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
  • 6. 6 Publishing Components 12 March 2015 NTTS 2015, Brussels, 10-12 March 2015
  • 7. 7 Consume: OpenCube browser 12 March 2015 NTTS 2015, Brussels, 10-12 March 2015 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.
  • 8.  Visualization of RDF data cubes on a map.  It supports:  Markers  Bubble  Choropleth maps 8 Consume: OpenCube MapView 12 March 2015 NTTS 2015, Brussels, 10-12 March 2015
  • 9.  Visualisation of analysis results (charts & tables)  Reuse of analysis results: preserving R output as linked data 12 March 2015 NTTS 2015, Brussels, 10-12 March 2015 9 Consume: Integration with R
  • 10. 12 March 2015 NTTS 2015, Brussels, 10-12 March 2015 10 Consume: Other Visualizations Analytics and ReportingVisualization and Exploration Stock chart
  • 11.  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) 11 Linking Statistical Data 12 March 2015 NTTS 2015, Brussels, 10-12 March 2015
  • 12. 12 Example: Start with an initial cube 12 March 2015 NTTS 2015, Brussels, 10-12 March 2015
  • 13. 13 Example: Discover & Select compatible cubes 12 March 2015 NTTS 2015, Brussels, 10-12 March 2015
  • 14. 14 Example: Browse an expanded view of the initial cube 12 March 2015 NTTS 2015, Brussels, 10-12 March 2015
  • 15.  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) 15 Conclusions 12 March 2015 NTTS 2015, Brussels, 10-12 March 2015
  • 16.  For more information  http://opencube-project.eu  http://opencube-toolkit.eu 16 Questions? 12 March 2015 NTTS 2015, Brussels, 10-12 March 2015  OpenCube consortium