v1.1.1

This release fixes:

1. pypi.org metadata — the list of supported patterns and usage example for afd is updated
2. requirements — cli needs desbordante-core 2.2.0

v1.1.0

This release is for Desbordante core package 2.2.0.

• Added support for discovery of soft functional dependencies and corellations in the CLI.
• Added discovery of many types of approximate functional dependencies in the CLI. For more information check the release notes of the core package.
• Added support for UCC and AUCC mining in the CLI.
• Added support for the HPIValid algorithm for discovery of UCCs in the CLI. To the best of our knowledge, currently it is the most performant algorithm for this task, so we made it the default one.
• Added support for numerical dependency validation in the CLI.
• Added support for differential dependency mining in the CLI.
• Added support for probabilistic functional dependency validation in the CLI.

v1.0.1

License Change to AGPL-3

Full Changelog: 1.0.0...1.0.1

v1.0.0

Desbordante CLI

Desbordante is a high-performance data profiler that is capable of discovering and validating many different patterns in data using various algorithms.

The Discovery task is designed to identify all instances of a specified pattern type of a given dataset.

The Validation task is different: it is designed to check whether a specified pattern instance is present in a given dataset. This task not only returns True or False, but it also explains why the instance does not hold (e.g. it can list table rows with conflicting values).

The currently supported data patterns are:

• Functional dependency variants:
  • Exact functional dependencies (discovery and validation)
  • Approximate functional dependencies, with g₁ metric (discovery and validation)
  • Probabilistic functional dependencies, with PerTuple and PerValue metrics (discovery)
• Graph functional dependencies (validation)
• Conditional functional dependencies (discovery)
• Inclusion dependencies (discovery)
• Order dependencies:
  • set-based axiomatization (discovery)
  • list-based axiomatization (discovery)
• Metric functional dependencies (validation)
• Fuzzy algebraic constraints (discovery)
• Unique column combinations:
  • Exact unique column combination (discovery and validation)
  • Approximate unique column combination, with g₁ metric (discovery and validation)
• Association rules (discovery)

For more information about the supported patterns check the main repo.

What's Changed

• Moved CLI to separated repository
• Add CLI for Split algorithm for discovery of Differential Dependencies by @MichaelS239 in #1

Full Changelog: https://github.com/Desbordante/desbordante-cli/commits/1.0.0