INODE – Intelligent Open Data Exploration (EU Horizon 2020)
At a glance
- Project leader : Prof. Dr. Kurt Stockinger
- Project team : Prof. Dr. Martin Braschler, Ursin Brunner, Catherine Kosten, Ana-Claudia Sima, Ellery Smith
- Project budget : EUR 5'732'000
- Project status : ongoing
- Funding partner : EU and other international programmes (Horizon 2020 / Projekt-Nr. 863410)
- Project partner : ATHENA Research, French National Centre for Scientific Research CNRS, Fraunhofer-Gesellschaft, Free University of Bozen-Bolzano, Infili Technologies P.C., Max-Planck-Gesellschaft, SIRIS Academic SL, Swiss Institute of Bioinformatics SIB
- Contact person : Kurt Stockinger
Description
Data growth and availability as well as data democratization
have radically changed data exploration in the last 10 years. Many
different data sets, generated by users, systems and sensors, are
continuously being collected. These data sets contain information
about scientific experiments, health, energy, education etc., and
they are highly heterogeneous in nature, ranging from highly
structured data in tabular form to unstructured text, images or
videos. Furthermore, especially online content, is no longer the
purview of large organizations. Open data repositories are made
public and can benefit more types of users, from analysts exploring
data sets for insight, scientists looking for patterns, to
dashboard interactors and consumers looking for information. As a
result, the benefit of data exploration becomes increasingly more
prominent. However, the volume and complexity of data make it
difficult for most users to access data in an easy way.
In this project we propose INODE – Intelligent Open Data
Exploration. The core principle of INODE is that users should
interact with data in a more dialectic and intuitive way similar to
a dialog with a human. To achieve this principle, INODE will offer
a suite of agile, fit-for purpose and sustainable services for
exploration of open data sets that help users (a) link and leverage
multiple datasets, (b) access and search data using natural
language, using examples and using analytics (c) get guidance from
the system in understanding the data and formulating the right
queries, and (d) explore data and discover new insights through
visualizations.
Our service offering is formed by and will initially respond to the
needs of large and diverse scientific communities brought by our
three use case providers: (a) Cancer Biomarker Research - SIB Swiss
Institute of Bioinformatics, Switzerland, (b) Research and
Innovation Policy Making - SIRIS, Spain, and (c) Astrophysics - Max
Planck Institute for Extraterrestrial Physics, Germany.
Further information
Publications
-
Brunner, Ursin; Stockinger, Kurt,
2021.
ValueNet : a natural language-to-SQL system that learns from database information [paper].
In:
Proceedings of the 37th ICDE.
International Conference on Data Engineering (ICDE), Chania, Greece, 19-22 April 2021.
IEEE.
Available from: https://doi.org/10.21256/zhaw-22000
-
Deriu, Jan Milan; Mlynchyk, Katsiaryna; Schläpfer, Philippe; Rodrigo, Alvaro; von Grünigen, Dirk; Kaiser, Nicolas; Stockinger, Kurt; Agirre, Eneko; Cieliebak, Mark,
2020.
A methodology for creating question answering corpora using inverse data annotation [paper].
In:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.
ACL 2020, Virtual, 5-10 July 2020.
Association for Computational Linguistics.
pp. 897-911.
Available from: https://doi.org/10.18653/v1/2020.acl-main.84