AI CLUSTERING TECHNIQUES: A NEW APPROACH IN HORIZONTAL FRAGMENTATION OF CLASSES WITH COMPLEX ATTRIBUTES AND METHODS IN OBJECT ORIENTED DATABASES

Abstract. Horizontal fragmentation plays an important role in the design phase of Distributed Databases. Complex class relationships: associations, aggregations and complex methods, require fragmentation algorithms to take into account the new problem dimensions induced by these features of the object oriented models. We propose in this paper a new method for horizontal partitioning of classes with complex attributes and methods, using AI clustering techniques. We provide quality and performance evaluations using a partition evaluator function and we prove that fragmentation methods handling complex interclass links produce better results than those ignoring these aspects. 1