INODE: Building an End-to-End Data Exploration System in Practice [Extended Vision]

TL;DR

INODE is an end-to-end data exploration system that combines machine learning and semantics to facilitate accessible, guided, and integrated data discovery across diverse scientific domains.

Contribution

The paper presents INODE, a comprehensive platform integrating data modeling, linking, natural language querying, guidance, and visualization for effective data exploration.

Findings

Demonstrated in cancer biomarker research, policy making, and astrophysics.

Accessible to both scientific communities and the public.

Facilitates discovery of new insights through integrated tools.

Abstract

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.