Titan: a high-performance remote-sensing database

Abstract

There are two major challenges for a high performance remote sensing database. First, it must provide low latency retrieval of very large volumes of spatio temporal data. This requires effective declustering and placement of a multidimensional dataset onto a large disk farm. Second, the order of magnitude reduction in data size due to post processing makes it imperative, from a performance perspective, that the post processing be done on the machine that holds the data. This requires careful coordination of computation and data retrieval. The paper describes the design, implementation and evaluation of Titan, a parallel shared nothing database designed for handling remote sensing data. The computational platform for Titan is a 16 processor IBM SP-2 with four fast disks attached to each processor. Titan is currently operational and contains about 24 GB of AVHRR data from the NOAA-7 satellite. The experimental results show that Titan provides good performance for global queries and interactive response times for local queries.