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Nowadays parallel object-relational DBMS are envisioned as the next great wave, but there is still a lack of efficient implementation concepts for some parts of the proposed functionality. Thus one of the current goals for parallel object-relational DBMS is to move towards higher performance. In this paper we develop a framework that allows to process user-defined functions with data parallelism. We will describe the class of partitionable functions that can be processed parallelly. We will also propose an extension which allows to speed up the processing of another large class of functions by means of parallel sorting. Functions that can be processed by means of our techniques are often used in decision support queries on large data volumes, for example. Hence a parallel execution is indispensable.
Jaedicke et al. (Mon,) studied this question.
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