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.

Department of Computer Science and Engineering Texas A&M University College Station, TX 77840, USA Abstract PyODDS is an end-to-end Py thon system for O utlier D etection with Database Support. It provides various outlier detection algorithms which meet the demands for users in different fields, with or without 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. Keywords: anomaly detection, end-to-end system, outlier detection, deep learning, machine learning, data mining, full stack system, data visualization 1. Introduction Outliers refer to the objects with patterns or behaviors that are significantly rare and different with the rest of majorities.