  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.
