A tool for nesting and clustering large objects

In implementations of non-standard database systems, large objects are often embedded within an aggregate of different types, i.e. a tuple. For a given size and access probability of a large object, query performance depends on its representation: either inlined within the aggregate or swapped out to a separate object. Furthermore, the implementation of complex data models often requires nested large objects, and access performance is highly influenced by the clustering strategy followed to store the resulting tree of large objects. In this paper we describe a large object extension which automatically clusters nested large objects. A rank function is developed which indicates the suitability of a large object being inserted into a given cluster. We present two clustering algorithms of different run-time complexity, both using the rank function, and a series of simulations is performed to compare them to each other as well as to two trivial ones. One of the algorithms proves to compute the most efficient clustering in all tests.