semvec/pypi-packages huggingface dataset:
RoleMiner 0.1.1
Utilities for Role Based Access Control
License: MIT

# RoleMiner

Python Implementation of the <b>FastMiner</b> and <b>Optimal Boolean Matrix Decomposition/RMP</b> algorithms for implementing <i>Role Based Access Control</i>, as described in the following papers:

- [<i>RoleMiner: Mining roles using subset enumeration</i>](https://www.researchgate.net/publication/221609861_RoleMiner_Mining_roles_using_subset_enumeration)
- [<i>Optimal Boolean Matrix Decomposition: Application to Role Engineering</i>](https://ieeexplore.ieee.org/document/4497438)

## Overview

The objective of Role Based Access Control (RBAC) is to determine the "best" set of roles that accurately describes user access without overfitting. In many ways this is similar to signal processing where "noise" (or one off permissions/entitlements) must be removed before the analysis is conducted, and then a series of candidate roles is generated on that cleaned data, since an exhaustive search of all possible roles is generally not possible (on the order of 2^n). Once the Candidate Roles are found, we can further structure Basic Role Mining Problem (RMP) beyond just "Find the smallest number of roles that describes the cleaned data" by relaxing constraints (aka [Regularization](https://en.wikipedia.org/wiki/Regularization_(mathematics)
 "Regularization")). 

 Possible regularizers in layman's terms could be somethign like the following:
 - "Roles must have at least 5 different entitlements, or we don't consider it a valid role"
 - "We want to ignore roles are only valid for less than 3 users, except the admin role"

Broadly, iterating through this process of generating candidate roles and iterating through the best choices with different regularization terms is very similar to [Simulated Annealing](https://en.wikipedia.org/wiki/Simulated_annealing "Simulated Annealing").

## Installation

Install the latest version of the RoleMiner code & dependencies with pip:

	$ pip install -U RoleMiner
ksj 1.0.0
This library will make the Kokudo Suuchi Jouhou API easy to use.
License: MIT

# ksj

[![CircleCI](https://circleci.com/gh/nigimitama/ksj/tree/master.svg?style=svg)](https://circleci.com/gh/nigimitama/ksj/tree/master)

[国土数値情報ダウンロードサービス](http://nlftp.mlit.go.jp/ksj/index.html)のWeb APIを簡単に扱うためのPythonパッケージです。

データ分析での利用を想定しており、本パッケージのメソッドの返り値はpandasやgeopandasのオブジェクトになります。



## インストール方法

```
pip install ksj
```

⚠️Windowsの場合[geopandasの依存パッケージ](http://geopandas.org/install.html#installing-with-pip)がpipではインストールできないため、**先にそちらをインストールする必要があります。** ご注意ください。

UNIX系OSの場合、`pip install ksj`だけでgeopandasもインストールされます。



## ドキュメント

使用方法は[ksj documentation](https://nigimitama.github.io/ksj/)をご覧ください



## 利用上の注意

APIの利用やAPIで得られる国土数値情報の利用にあたっては、国土数値情報ダウンロードサービスの利用約款および同Web APIの利用規約をご確認の上ご利用ください。

- http://nlftp.mlit.go.jp/ksj/other/yakkan.html
- http://nlftp.mlit.go.jp/ksj/api/about_api.html