Outlier detection is an important task for various data mining applications. Current outlier detection techniques are often manually designed for specific domains, requiring large human efforts of database setup, algorithm selection, and hyper-parameter tuning. To fill this gap, we present PyODDS, an automated end-to-end Python system for Outlier Detection with Database Support, which automatically optimizes an outlier detection pipeline for a new data source at hand. Specifically, we define the search space in the outlier detection pipeline, and produce a search strategy within the given search space. PyODDS enables end-to-end executions based on an Apache Spark backend server and a light-weight database. It also provides unified interfaces and visualizations for users with or without data science or machine learning background. In particular, we demonstrate PyODDS on several real-world datasets, with quantification analysis and visualization results.

PyODDS是一个自动化的Python系统，它通过数据库支持自动优化新数据源上的异常检测管道。该系统能够在Apache Spark背端服务器和轻量级数据库的支持下实现端到端的执行以及面向具有或不具有数据科学或机器学习背景的用户提供统一的接口和可视化。

PyODDS：具备自动化机器学习功能的端到端异常检测系统