@Article{moon:declustering,
  author = {Bongki Moon and Joel H. Saltz},
  title = {Scalability Analysis of Declustering Methods for for
  Multidimensional Range Queries},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  year = {1997},
  note = {To appear},
  URL = {ftp://hpsl.cs.umd.edu/pub/papers/ieee_tkde.ps.Z},
  keywords = {parallel I/O, parallel database, declustering, pario-bib},
  abstract = {Efficient storage and retrieval of multi-attribute datasets have
  become one of the essential requirements for many data-intensive
  applications. The Cartesian product file has been known as an effective
  multi-attribute file structure for partial-match and best-match queries.
  Several heuristic methods have been developed to decluster Cartesian product
  files across multiple disks to obtain high performance for disk accesses.
  Though the scalability of the declustering methods becomes increasingly
  important for systems equipped with a large number of disks, no analytic
  studies have been done so far. In this paper we derive formulas describing
  the scalability of two popular declustering methods Disk Modulo and Fieldwise
  Xor for range queries, which are the most common type of queries. These
  formulas disclose the limited scalability of the declustering methods and are
  corroborated by extensive simulation experiments. From the practical point of
  view, the formulas given in this paper provide a simple measure which can be
  used to predict the response time of a given range query and to guide the
  selection of a declustering method under various conditions.}
}