Intuitiveness

Intuitiveness as the Next Stage of Dataset Design
Granularity and complexity for tabular data

What is Intuitiveness?

Intuitiveness is a methodology and Python package for transforming raw, complex datasets into purpose-built data that directly answers your questions.

The method works through a descent-ascent cycle:

• Descent (L4 -> L0): Strip away complexity to find the core truth
• Ascent (L0 -> L3): Rebuild with YOUR intent, adding only relevant dimensions

The 5 Levels of Abstraction

Installation

pip install intuitiveness

With optional extras:

pip install intuitiveness[graph]      # Neo4j/Memgraph knowledge graphs
pip install intuitiveness[postgres]   # Durable session store on PostgreSQL
pip install intuitiveness[all]        # Everything

Quick Start

from intuitiveness import Level4Dataset, NavigationSession

# Load your data
import pandas as pd
tables = {"population.csv": pd.read_csv("population.csv")}

# Create a session and start navigating
l4 = Level4Dataset(tables)
session = NavigationSession(l4)
print(f"Level: {session.current_level.value}")  # 4

# Descend through abstraction levels
session.descend()  # L4 -> L3 (entity graph)
session.descend()  # L3 -> L2 (domain categories)
session.descend()  # L2 -> L1 (feature vector)
session.descend()  # L1 -> L0 (core datum)

# Ascend back with new intent
session.ascend()   # L0 -> L1 (enriched vector)
session.ascend()   # L1 -> L2 (new dimensions)
session.ascend()   # L2 -> L3 (linked graph)

Features

• Descent-Ascent Engine: Navigate 5 abstraction levels with full lineage tracking
• Navigation Tree: Branch, time-travel, and compare alternative data redesigns
• MCP Integration: Search data.gouv.fr and World Bank Data360 via Model Context Protocol
• AI-Assisted Transitions: LLM suggests entity joins, domain categories, aggregations, and linkages
• Session Persistence: Save/restore navigation sessions (file or PostgreSQL backend)
• Complexity Measurement: Quantify dataset complexity across abstraction levels

Web Application

The full interactive experience is available as a deployed web app with a React frontend and FastAPI backend. See the repository for deployment instructions.

Scientific Article

This work is documented in a peer-reviewed research paper:

Sarazin, A., Mourey, M. (2026). Intuitiveness as the Next Stage of Dataset Design: granularity and complexity for tabular data. Harvard Data Science Review (under review).

• PDF: scientific_article/Intuitiveness.pdf

Acknowledgments

Part of the Dataflow research project, funded by:

• Datactivist
• UNESCO Chair in AI and Data Science for Society

_{Designed by Arthur Sarazin & Mathis Mourey}