DOEF: A Dynamic Object Evaluation Framework

TL;DR

This paper introduces DOEF, a flexible framework for evaluating how object databases adapt to changing access patterns, enabling better assessment of dynamic clustering algorithms.

Contribution

It presents a novel, extensible framework for testing database performance under dynamic access patterns, addressing a gap in existing evaluation methods.

Findings

DOEF effectively measures adaptability of clustering algorithms

Four clustering algorithms were compared using DOEF

Results highlight differences in algorithm responsiveness to pattern changes

Abstract

In object-oriented or object-relational databases such as multimedia databases or most XML databases, access patterns are not static, i.e., applications do not always access the same objects in the same order repeatedly. However, this has been the way these databases and associated optimisation techniques like clustering have been evaluated up to now. This paper opens up research regarding this issue by proposing a dynamic object evaluation framework (DOEF) that accomplishes access pattern change by defining configurable styles of change. This preliminary prototype has been designed to be open and fully extensible. To illustrate the capabilities of DOEF, we used it to compare the performances of four state of the art dynamic clustering algorithms. The results show that DOEF is indeed effective at determining the adaptability of each dynamic clustering algorithm to changes in access pattern.