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 Research, 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.

一个成熟的数据探索系统必须将不同的访问方式与一个强大的概念相结合，在探索过程中引导用户，对数据发现和数据链接都具有反应性和预期性。这样的系统是我们社区满足具有不同领域和数据科学专业知识的用户的真正机会。

我们介绍了 INODE——一种端到端的数据探索系统——它一方面利用机器学习，另一方面利用语义来实现数据管理 (DM)。我们的愿景是开发一个经典的统一、全面的平台，提供对开放数据集的广泛访问，并在癌症生物标志物研究、研究和创新政策制定以及天体物理学领域的三个重要用例中进行展示。INODE 在 (a) 数据建模和链接、(b) 使用自然语言的集成查询处理、(c) 指导和 (d) 通过可视化进行数据探索方面提供可持续服务，从而促进用户发现新的见解。我们证明了我们的系统对于从大型科学界到公众的广泛用户来说是独一无二的。最后，