Some Approaches to Index Design for Cube Forest.

Theodore Johnson, Dennis Shasha: Some Approaches to Index Design for Cube Forest. IEEE Data Eng. Bull. 20(1): 27-35(1997)

@article{DBLP:journals/debu/JohnsonS97,
  author    = {Theodore Johnson and
               Dennis Shasha},
  title     = {Some Approaches to Index Design for Cube Forest},
  journal   = {IEEE Data Eng. Bull.},
  volume    = {20},
  number    = {1},
  year      = {1997},
  pages     = {27-35},
  ee        = {db/journals/debu/JohnsonS97.html},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

The paradigmatic view of data in decision support consists of a set of dimensions (e.g., location, product, time period, ...), each encoding a hierarchy (e.g., location has hemisphere, country, state/province, ..., block) or nearly a hierarchy (there are exceptions: weeks don't fit into months and sizes don't fit into colors). Typical queries consist of aggregates over a quantifiable attribute (e.g., sales) as a function of at most one attribute in each dimension of this "data cube." For example, find the sum of all sales of blue polo shirts in Palm Beach during the last quarter. We discuss a few different alternatives to solve this problem including a data structure called cube forests and its elaboration hierarchically split cube forests that exploit the hierarchical nature of the data to save space. We also discuss some design considerations.

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References

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[6]: Venky Harinarayan, Anand Rajaraman, Jeffrey D. Ullman: Implementing Data Cubes Efficiently. SIGMOD Conference 1996: 205-216
[7]: Vincent Y. Lum: Multi-Attribute Retrieval with Combined Indexes. Commun. ACM 13(11): 660-665(1970)
[8]: Patrick E. O'Neil: Model 204 Architecture and Performance. HPTS 1987: 40-59