Opportunistic Prioritised Clustering Framework for Improving OODBMS

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Provided by: La Trobe University
Topic: Big Data
Format: PDF
The current rate of performance improvement for CPUs is much higher than that for memory or disk I/O. In object oriented database management systems, clustering has proven to be one of the most effective performance enhancement techniques. Existing clustering algorithms are mainly static, that is re-clustering the object base when the database is off-line. However, this type of re-clustering cannot be used when 24-hour database access is required. In such situations dynamic clustering is necessary, since it can re-cluster the object base while the database is in operation.
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