In the era of Big Data, being able to work with multidimensional arrays in a robust and consistent manner as the number and variety of dimensions increase, is just as important as being able to handle the large volumes inherent to this type of data. Usually, array analytics is carried out to extract meaningful information for further applications, e.g., slicing and subsetting. While domain-specific dimensions, which are beyond spatio-temporal, underlie rich domain anchor semantics, assigning consistent dimension schema for Points Of Interest (POI) across multidisciplinary data sets is challenging. New compositions of CRSs need to be constructed on the fly by a heterogeneous community with different backgrounds and applications in mind, consequently, linking dimensions via different resolvers to drive away dimension fragments from high-dimensional spaces. We propose to identify dimensions via a linked resolver approach. Such an approach allows CRSs to be referred to and looked up across multidisciplinary applications. Finally, we present a planetary use case, and specification- and scenario-based testing results to validate our approach.
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