=Paper=
{{Paper
|id=Vol-2050/dao-paper4
|storemode=property
|title=The OBDA-Based “Observatory of Research and Innovation” of the Tuscany Region
|pdfUrl=https://ceur-ws.org/Vol-2050/DAO_paper_4.pdf
|volume=Vol-2050
|authors=Alessandro Mosca,Bernardo Rondelli,Guillem Rull
|dblpUrl=https://dblp.org/rec/conf/jowo/MoscaRR17
}}
==The OBDA-Based “Observatory of Research and Innovation” of the Tuscany Region==
https://ceur-ws.org/Vol-2050/DAO_paper_4.pdf
The OBDA-based “Observatory of
Research and Innovation” of the Tuscany
Region
Alessandro Mosca, Bernardo Rondelli and Guillem Rull
SIRIS Lab, Research division of SIRIS Academic, Barcelona, Spain
{name.surname}@sirisacademic.com
Abstract. The Tuscany’s Observatory of Research and Innovation portal is an
instrument to promote more transparent and inclusive governance in the region.
We show its interactive dashboard and underlying SPARQL endpoint, powered by
SIRIS Academic’s UNiCS platform, which integrates Open Data on the Higher
Education & Research field, following the Ontology-Based Data Access approach.
Keywords. OBDA, Higher Education & Research, Data-driven policies, Interactive
Data Visualisation
1. Introduction
In line with the orientation adopted by the EU in its Europe 2020 strategy, the Italian
region of Tuscany has defined a set of policies aimed at supporting the Higher Education
and Research (HE&R) system, and promoting innovation in the Tuscan territory. Tus-
cany has provided the Regional Research and Innovation Observatory as a tool to sup-
port the implementation of the Regional Development Program (PRS 20016/201 ). The
PRS is the cornerstone of regional policies: “[A] tool that expresses a vision for the fu-
ture of Tuscany and proposes constructive dialogue with the actors of the territory” (E.
Rossi, Region’s President, PRS introductory speech). This tool has the ambition to com-
municate and enhance the strengths of the research system, and to host information on
research, innovation and higher education.
Within the Observatory, and in support of the PRS, the Tuscany Region has decided
to have an information dashboard capable of integrating HE&R data, keeping them up
to date, and supporting policy makers in designing their policies. This paper shows the
Observatory’s interactive dashboard, currently located at toscanaopenresearch.it2 ,
and the underlying SPARQL endpoint, which is powered by SIRIS Academic’s UNiCS
platform [5], a system that integrates Open Data on the HE&R field and makes them
accessible to users through a unified domain ontology, following the so-called Ontology-
Based Data Access (OBDA) approach [6,3].
1 https://goo.gl/6VY4Co
2 Guest users can login with username/password: dao2017/dao2017
KPIs EXPLORER
Local Governments &
Public Administration
Department HE&R Open Data
Ontology
KPIs EXPLORER
Regional Government
Federation Layer
Directorate
Mappings
Tuscany Internal data
Citizens
FULL-FLEDGE ACCESS POINT
Compliant SPARQL protocol service
END-USERs DATA VISUALISATIONs, QUERY SYSTEM OBDA LAYER DATA SOURCEs
Figure 1. UNiCS platform architecture tailored for the Tuscany’s Observatory of Research and Innovation
The paper is structured as follows. First, Section 2 introduces the underlying plat-
form that provides data to the Observatory, then Section 3 describes the Observatory’s
interactive dashboard.
2. The UNiCS Platform
University Analytics (UNiCS) integrates open data repositories about HE&R in Europe
and makes them available via a dedicated SPARQL endpoint [9,8]. Queries are posed in
terms of a domain ontology that provides a homogeneous view of the otherwise disparate
integrated datasets. While data are originally stored in relational databases (DBs), UNiCS
users see them as RDF data, the standard data model in Linked Data3 . This is made
possible by -ontop- [1], an OBDA system that allows querying relational DBs as virtual
RDF graphs using SPARQL. Given the domain ontology, and an R2RML [7] mapping
description that connects the ontology with the underlying DBs, -ontop- translates the
users’ SPARQL queries into SQL ones that are then run on the federated DB.
In the context of the Tuscany’s Observatory, the core architecture of UNiCS includes
a relational DB into which the different Italian and European open data repositories have
been integrated, as per the Data Exchange [2] approach. The reason why these datasets
have been copied into a single relational DB is that they are not available as proper,
queryable DBs, but only as downloadable CSVs, so moving them into a more suitable
storage is required. The datasets include official Italian student and researcher data4 com-
ing from the MIUR (Ministero dell’Istruzione, dell’Università e della Ricerca), and Eu-
ropean data on FP7 and H2020 research projects [4]. An extension to this architecture
is planned to incorporate internal data managed by the Tuscany Region, which will be
federated with the UNiCS DB, and mapped into the UNiCS domain ontology. The ar-
chitecture is depicted in Figure 1. (Being able to integrate not just relational sources but
3 https://www.w3.org/DesignIssues/LinkedData.html
4 Ministero dell’Istruzione, dell’Università e della Ricerca: (i) Anagrafe nazionale studenti:
anagrafe.miur.it; (ii) Cerca università: cercauniversita.cineca.it
Figure 2. Interactive visualisation that shows a pop-up windows with additional information as the user
mouses over.
also datasets that are behind given SPARQL endpoints is an -ontop- extension we are
currently working on in collaboration with the KRDB Research Centre5 ).
3. Data visualisation
Currently, the Observatory consists mainly of an interactive dashboard, hosting data vi-
sualisations (co-designed with the relevant stakeholders) fed by the underlying UNiCS
SPARQL endpoint. The visualisations are generated in real time by Javascript code run-
ning on the user’s browser, which interrogates the endpoint for the necessary data. Vi-
sualisations are interactive: the user can click on different components to drill down on
the results being displayed, as well as apply filters to focus on a particular data subset.
Pop-up windows are also displayed with additional information that is not originally pro-
vided by the visual representation of the data, once the user selects a specific item in it.
As an example, Figure 2 shows the distribution of graduated students per Italian bache-
lor faculty, and the different colours are meant to represent the overall number of years
they spent at the university. Once the user mouses over the graphic of a given faculty, a
window appears with a summarized view of the data behind, showing numbers that are
not visible in the original visualisation. Users can either download the data behind each
visualisation or copy and paste the queries which generate those data (see Figure 3), and
execute them, possibly modified according to new specific needs.
The dashboard is now conceptually divided into four main sections: Teachers &
researchers (showing distributions per gender, age, and disciplinary sectors), Teaching
(on student data, with provenance, success rates per bachelor and master degrees), Re-
search at universities (on EU funded projects, relative/absolute budgets, targeted to pub-
lic organisations), and Research at private companies (on EU/regional funded projects,
targeted to private organisations).
The Observatory’s portal also includes a dedicated SPARQL endpoint and the
LODE-powered documentation of the relative domain ontology6 . The endpoint includes
5 http://www.inf.unibz.it/krdb/, Free University of Bozen-Bolzano, Italy.
6 http://34.250.237.252/toscana/sparql/docs/index.html
Figure 3. The dashboard allows users to see the SPARQL queries behind each visualisation, and also gives
them the option to download the combined result of the queries in CSV format.
a library of pre-defined queries that either refer to the dashboard visualisations or have
been collaboratively specified with the managers of the regions to satisfy specific needs
and strategic demands. Users that are not familiar with SPARQL can then profit of the
library, modify existing queries and execute them. The portal’s visualisations and the
SPARQL endpoint can be used by policy makers to get a better understanding of the
current situation, and both monitor the effectiveness of recent policies and also be able
to design new policies based on evidence rather than intuition.
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