Opportunistic Prioritised Clustering Framework (OPCF)

  • Zhen He
  • Alonso Márquez
  • Stephen Blackburn
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1944)


Ever since the ‘early days’ of database management systems, clustering has proven to be one of the most effective performance enhan- cement techniques for object oriented database management systems. The bulk of the work in the area has been on static clustering algorithms which re-cluster the object base when the database is static. However, this type of re-clustering cannot be used when 24-hour database access is required. In such situations dynamic clustering is required, which allows the object base to be reclustered while the database is in operation. We believe that most existing dynamic clustering algorithms lack three im- portant properties. These include: the use of opportunism to imposes the smallest I/O footprint for re-organisation; the re-use of prior research on static clustering algorithms; and the prioritisation of re-clustering so that the worst clustered pages are re-clustered first. In this paper, we present OPCF, a framework in which any existing static clustering algorithm can be made dynamic and given the desired properties of I/O opportunism and clustering prioritisation. In addition, this paper presents a perfor- mance evaluation of the ideas suggested above.The main contribution of this paper is the observation that existing static clustering algorithms, when transformed via a simple transformation framework such as OPCF, can produce dynamic clustering algorithms that out-perform complex existing dynamic algorithms, in a variety of situations. This makes the solution presented in this paper particularly attractive to real OODBMS system implementers who often prefer to opt for simpler solutions.


Cluster Algorithm Object Base Dynamic Cluster Object Graph Cluster Unit 
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 2001

Authors and Affiliations

  • Zhen He
    • 1
  • Alonso Márquez
    • 1
  • Stephen Blackburn
    • 2
  1. 1.Department of Computer ScienceThe Australian National UniversityCanberraAustralia
  2. 2.Department of Computer ScienceUniversity of MassachusettsAmherstUSA

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