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

arXiv: https://arxiv.org/abs/2104.04194 · HTML: https://mlsanity.com/paper/2104.04194/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 53 references that are in the map.
- First posted on arXiv 9 Apr 2021.

## Builds on (most cited first, 10 of 10)

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [The Case for Learned Index Structures](https://mlsanity.com/paper/1712.01208/) (2017)
- [A Comparative Survey of Recent Natural Language Interfaces for Databases](https://mlsanity.com/paper/1906.08990/) (2019)
- [KeystoneML: Optimizing Pipelines for Large-Scale Advanced Analytics](https://mlsanity.com/paper/1610.09451/) (2016)
- [A Methodology for Creating Question Answering Corpora Using Inverse Data Annotation](https://mlsanity.com/paper/2004.07633/) (2020)
- [Question Answering on Linked Data: Challenges and Future Directions](https://mlsanity.com/paper/1601.03541/) (2016)
- [IDEBench: A Benchmark for Interactive Data Exploration](https://mlsanity.com/paper/1804.02593/) (2018)
- [SQL Query Completion for Data Exploration](https://mlsanity.com/paper/1802.02872/) (2018)
- [Efficient Handling of SPARQL OPTIONAL for OBDA (Extended Version)](https://mlsanity.com/paper/1806.05918/) (2018)
- [Enriching Ontology-based Data Access with Provenance (Extended Version)](https://mlsanity.com/paper/1906.00179/) (2019)

## Built on by (most cited first, 1 of 1)

- [ScienceBenchmark: A Complex Real-World Benchmark for Evaluating Natural Language to SQL Systems](https://mlsanity.com/paper/2306.04743/) (2023)

## Abstract (arXiv)

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.