Evaluation of a Dynamic Tree Structure for Indexing Query Regions on Streaming Geospatial Data

Most recent research on querying and managing data streams has concentrated on traditional data models where the data come in the form of tuples or XML data. Complex types of streaming data, in particular spatio-temporal data, have primarily been investigated in the context of moving objects and location-aware services. In this paper, we study query processing and optimization aspects for streaming RemotelySensed Imagery (RSI) data. Streaming RSI is typical for the vast amount of imaging satellites orbiting the Earth, and it exhibits certain characteristics that make it very attractive to tailored query optimization techniques. Our approach uses a Dynamic Cascade Tree (DCT ) to (1) index spatio-temporal query regions associated with continuous user queries and (2) efficiently determine what incoming RSI data is relevant to what queries. The DCT supports the processing of different types of RSI data, ranging from point data to more general spatial extents in which the incoming imagery can be single pixels, rows of pixels, or discrete parts of images. The DCT exploits spatial trends in incoming RSI data to efficiently filter the data of interest to the individual query regions. Experimental results using random input and Geostationary Operational Environmental Satellite (GOES) data give a good insight into processing streaming RSI and verify the efficiency and utility of the DCT .