Abstract:
This work addresses data warehouse maintenance, i.e. how changes to autonomous, heterogeneous, and distributed sources should be detected and propagated to a warehouse. The research community has mainly addressed issues relating to the internal operation of data warehouse servers. Work related to data warehouse maintenance has received less attention and only a limited set of maintenance alternatives are considered while ignoring the autonomy and heterogeneity of sources. In this paper, we extend work on single source view maintenance to views with multiple heterogeneous sources. We present a tool (PAM) which allows for comparison of a large number of relevant maintenance policies under different configurations. Based on such analysis and previous studies we propose a set of heuristics to guide in policy selection. The quality of these heuristics is evaluated empirically using a test-bed developed for this purpose. This is done for a number of different criteria and for different data sources and computer systems. The performance gained using the policy selected through the heuristics is compared with the performance of all identified policies. Based on these experiments we claim that heuristic-based selections are good.
