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Recently skyline queries have gained considerable attention and are among the most important tools for multi-criteria analysis. In order to process all possible combinations of criteria along with their inherent analysis, researchers introduced and studied the notion of skycube. Simply put, a skycube is a pre-materialization of all possible subspaces with their associated skylines. An efficient skycube computation relies on the detection of redundancies in the different processing steps and enhanced result sharing between subspaces. Lately, the Orion algorithm was proposed to compute the skycube in a very efficient way. The approach relies on the derivation of skyline points over different subspaces. Nevertheless, because there are 2|D| - 1 subspaces (where D is the set of dimensions) in a skycube, the running time still grows exponentially with the number of dimensions and easily becomes intractable on real-world datasets. In this study, we detail the distribution of Orion within a filter-stream framework and we conduct an extensive set of experiments on large datasets collected from Twitter to demonstrate the efficiency of our method.