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It is demonstrated how linear computational time and storage efficientapproaches can be adopted when analyzing very large data sets. Moreimportantly, interpretation is aided and furthermore, basic processing iseasily supported. Such basic processing can be the use of supplementary, i.e.contextual, elements, or particular associations. Furthermore pixellated gridcell contents can be utilized as a basic form of imposed clustering. For agiven resolution level, here related to an associated m-adic ($m$ here is anon-prime integer) or p-adic ($p$ is prime) number system encoding, suchpixellated mapping results in partitioning. The association of a range ofm-adic and p-adic representations leads naturally to an imposed hierarchicalclustering, with partition levels corresponding to the m-adic-based andp-adic-based representations and displays. In these clustering embedding andimposed cluster structures, some analytical visualization and searchapplications are described

Linear Time Visualization and Search in Big Data using Pixellated Factor Space Mapping