PyODDS: An End-to-End Outlier Detection System

TL;DR

PyODDS is a comprehensive Python-based system for outlier detection that integrates multiple algorithms, supports database in-place analysis, and caters to users with varying levels of data science expertise.

Contribution

It introduces an end-to-end outlier detection system with database support, enabling in-database analysis and access to diverse algorithms including deep learning methods.

Findings

Supports in-database outlier detection without data transfer

Includes a wide range of statistical and deep learning algorithms

Accessible to users with different levels of data science background

Abstract

PyODDS is an end-to end Python system for outlier detection with database support. PyODDS provides outlier detection algorithms which meet the demands for users in different fields, w/wo data science or machine learning background. PyODDS gives the ability to execute machine learning algorithms in-database without moving data out of the database server or over the network. It also provides access to a wide range of outlier detection algorithms, including statistical analysis and more recent deep learning based approaches. PyODDS is released under the MIT open-source license, and currently available at (https://github.com/datamllab/pyodds) with official documentations at (https://pyodds.github.io/).