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.

Modern document management systems administer an increasing amount of data. Unfortunately traditional database technology cannot satisfy the resulting performance requirements. We report on how parallel database technology can contribute to make large and scalable document management systems work. Our discussion clearly shows that simply substituting a sequential database system for a parallel one does not necessarily lead to scalability and high performance. In contrast, detailed knowledge of the application and document management system is necessary to decide on the optimal architecture of the parallel database system, its query optimization and parallelization strategies, as well as on its data fragmentation scheme and load balancing properties. All discussions are exemplified referring to an existing document management system developed at our university that is currently migrated from a sequential database system to a parallel one.