@InProceedings{chang:titan,
  author = {Chialin Chang and Bongki Moon and Anurag Acharya and Carter Shock
  and Alan Sussman and Joel Saltz},
  title = {{Titan}: a High-Performance Remote-sensing Database},
  booktitle = {Proceedings of the Thirteenth International Conference on Data
  Engineering},
  year = {1997},
  month = {April},
  address = {Birmingham, U.K.},
  URL = {ftp://hpsl.cs.umd.edu/pub/papers/icde97-final.ps.Z},
  keywords = {parallel databases, satellite imagery, remote sensing, parallel
  I/O, pario-bib},
  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 multi-dimensional 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 postprocessing be done
  on the machine that holds the data. This requires careful coordination of
  computation and data retrieval. This paper describes the design,
  implementation and evaluation of {\em 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.}
}