Authors:  Sihem Amer-Yahia, Georgia Koutrika, Martin Braschler, Diego Calvanese, Davide Lanti, Hendrik Lücke-Tieke, Alessandro Mosca, Tarcisio Mendes de Farias, Dimitris Papadopoulos, Yogendra Patil, Guillem Rull, Ellery Smith, Dimitrios Skoutas, Srividya Subramanian, Kurt Stockinger

Published in: SIGMOD Record

Abstract:

A full-fledged data exploration system must combine different ac- cess modalities with a powerful concept of guiding the user in the exploration process, by being reactive and anticipative both for data discovery and for data linking. Such systems are a real opportunity for our community to cater to users with different domain and data science expertise.

We introduce INODE – an end-to-end data exploration system – that leverages, on the one hand, Machine Learning and, on the other hand, semantics for the purpose of Data Management (DM). Our vision is to develop a classic unified, comprehensive platform that provides extensive access to open datasets, and we demonstrate it in three significant use cases in the fields of Cancer Biomarker Re- search, Research and Innovation Policy Making, and Astrophysics. INODE offers sustainable services in (a) data modeling and linking, (b) integrated query processing using natural language, (c) guidance, and (d) data exploration through visualization, thus facilitating the user in discovering new insights. We demonstrate that our system is uniquely accessible to a wide range of users from larger scientific communities to the public. Finally, we briefly illustrate how this work paves the way for new research opportunities in DM.