Intelligent Open Data Exploration

Our Mission

A classic unified, comprehensive platform that provides extensive access to open datasets through natural language queries in the fields of Cancer Biomarker Research, Research and Innovation Policy Making and Astrophysics; for a wide range of users from larger scientific communities to public. 


Data growth and availability as well as data democratisation have radically changed data exploration in the last 10 years. INODE aims at simplifying access to data, by allowing a more dialectic and intuitive interaction with data using Natural Language Processing (NLP). The goal of INODE is to offer a suite of agile, fit-for-purpose and sustainable services for exploration of multiple open data sets; guidance for the users in understanding the data by providing insights into the data modal using visualisation techniques and offer suggestions of prospective datasets using recommender systems that can drive the user in formulating right NLP queries to find the right dataset efficiently; let user to explore the data extensively through in-built data analytic packages and to discover new insights/science through data visualisation.

Latest News !


INODE Services Applied at HackZurich 2021 !

HackZurich 2021 was Europe’s largest hackathon with about 1,500 enthusiastic participants from all over the world. The hackers had 40 hours to solve one of the 18 challenges. The three INODE team members Kate Kosten, Ursin Brunner and Kurt Stockinger teamed up with...

INODE at HackZurich !

Together with the Swiss Federal Statistical Office, INODE team members are participating at HackZurich, the largest hackathon in Europe.    

Let the Database Talk Back: Natural Language Explanations for SQL

Authors:    Eleftherakis, S., Gkini, O., & Koutrika, G. Published in:   2nd Workshop on Search, Exploration, and Analysis in Heterogeneous Datastores (SEA Data) 2021 Abstract:  Database interaction is often characterized as a non-trivial and time- consuming...

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 863410