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Exploiting the Multi-Append-Only-Trend Property of Historical Data in Data Warehouses

  • Hua-Gang Li
  • Divyakant Agrawal
  • Amr El Abbadi
  • Mirek Riedewald
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2750)

Abstract

Data warehouses maintain historical information to enable the discovery of trends and developments over time. Hence data items usually contain time-related attributes like the time of a sales transaction or the order and shipping date of a product. Furthermore the values of these time-related attributes have a tendency to increase over time. We refer to this as the Multi-Append-Only-Trend (MAOT) property. In this paper we formalize the notion of MAOT and show how taking advantage of this property can improve query performance considerably. We focus on range aggregate queries which are essential for summarizing large data sets. Compared to MOLAP data cubes the amount of pre-computation and hence additional storage in the proposed technique is dramatically reduced.

Keywords

Range Query Query Time Data Cube Sales Transaction Outlier Data Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Hua-Gang Li
    • 1
  • Divyakant Agrawal
    • 1
  • Amr El Abbadi
    • 1
  • Mirek Riedewald
    • 2
  1. 1.University of CaliforniaSanta BarbaraUSA
  2. 2.Cornell UniversityIthacaUSA

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