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, Farhad Nooralahzadeh, Ana-Claudia Sima, Ellery Smith, Yi Zhang
- Project budget : EUR 5'732'000
- Project status : completed
- 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
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.
5th International Conference on Natural Language and Speech Processing (ICNLSP), online, 16-17 December 2022.
ZHAW Zürcher Hochschule für Angewandte Wissenschaften.
Available from: https://doi.org/10.21256/zhaw-26147
Proceedings of the 34th SSDBM.
34th International Conference on Scientific and Statistical Database Management (SSDBM), Copenhagen, Denmark, 6 - 8 July 2022.
Association for Computing Machinery.
Available from: https://doi.org/10.1145/3538712.3538744
Amer-Yahia, Sihem; Koutrika, Georgia; Braschler, Martin; Calvanese, Diego; Lanti, Davide; Lücke-Tieke, Hendrik; Mosca, Alessandro; Mendes de Farias, Tarcisio; Papadopoulos, Dimitris; Patil, Yogendra; Rull, Guillem; Smith, Ellery; Skoutas, Dimitrios; Subramanian, Srividya; Stockinger, Kurt,
Available from: https://doi.org/10.21256/zhaw-23624
Brunner, Ursin; Stockinger, Kurt,
Proceedings of the 37th ICDE.
International Conference on Data Engineering (ICDE), Chania, Greece, 19-22 April 2021.
Available from: https://doi.org/10.21256/zhaw-22000
Available from: https://doi.org/10.1016/j.is.2021.101938
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics.
58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), online, 5-10 July 2020.
Association for Computational Linguistics.
Available from: https://doi.org/10.18653/v1/2020.acl-main.84