Opportunistic Prioritised Clustering Framework (OPCF)

Ever since the 'early days' of database management systems, clustering has proven to be one of the most effective performance enhancement 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 important 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 performance 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.