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

DJEnsemble: On the Selection of a Disjoint Ensemble of Deep Learning Black-Box Spatio-Temporal Models