  In this paper, we present a cost-based approach for the automatic selection
and allocation of a disjoint ensemble of black-box predictors to answer
predictive spatio-temporal queries. Our approach is divided into two parts --
offline and online. During the offline part, we preprocess the predictive
domain data -- transforming it into a regular grid -- and the black-box models
-- computing their spatio-temporal learning function. In the online part, we
compute a DJEnsemble plan which minimizes a multivariate cost function based on
estimates for the prediction error and the execution cost -- producing a model
spatial allocation matrix -- and run the optimal ensemble plan. We conduct a
set of extensive experiments that evaluate the DJEnsemble approach and
highlight its efficiency. We show that our cost model produces plans with
performance close to the actual best plan. When compared against the
traditional ensemble approach, DJEnsemble achieves up to $4X$ improvement in
execution time and almost $9X$ improvement in prediction accuracy. To the best
of our knowledge, this is the first work to solve the problem of optimizing the
allocation of black-box models to answer predictive spatio-temporal queries.
