  A full-fledged data exploration system must combine different access
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 Reearch,
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.
