In the last few years, there has been active research on aggregating advanced statistical measures in multidimensional data cubes from partitioned subsets of data. In this paper, we propose an online compression and aggregation scheme to support Bayesian estimations in data cubes based on the asymptotic properties of Bayesian statistics. In the proposed approach, we compress each data segment by retaining only the model parameters and a small amount of auxiliary measures. We then develop an aggregation formula that allows us to reconstruct the Bayesian estimation from partitioned segments with a small approximation error. We show that the Bayesian estimates and the aggregated Bayesian estimates are asymptotically equivalent
