Spatial Query Processing on AIS Data Streams in Data Stream Management Systems

Abstract

Spatio-temporal data streams from moving objects have become ubiquitous in the recent years, not only, but also in the maritime domain. The Automatic Identification System (AIS) is an important technology in the maritime domain that creates huge amounts of streaming moving object data and enables new use cases. The data streams can improve the situation awareness, help Vessel Traffic Services (VTSs) to get an overview of certain situations and detect upcoming critical situations automatically. For these use cases, queries have to be processed on data streams with continuous results and little delay. To reach this goal, Data Stream Management Systems (DSMSs) lay a foundation to process data streams, but lack the capabilities for spatio-temporal query processing. We tackle this research gap with techniques known from moving object databases and integrate those into the stream processing. We present a system that integrates the moving object algebra from moving object databases into the interval approach from data stream processing to run queries on AIS data. This new approach allows us to define very diverse spatio-temporal queries on AIS data streams, such as radius queries, k-nearest neighbors (kNN) queries as well as queries with moving polygons. Additionally, the approach allows us to use short-time prediction to detect situations before they occur, e. g., to avoid collisions. Our results show that the system is very flexible, offers a clear semantics and produces results on AIS streams with many vessels with low latency.