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Abstract

A data structure that uses one or more keys which are members of a multidimensional composite key to partition sparse data (FIG. 2A, FIG. 2B) into a hypercube like structure (FIG. 1). Only the data structure produced by the method in this invention is actually better than a hypercube since all involved keys can have true symmetric access as opposed to just the keys that have the highest sort order significance. Equivalent query efficiency could only be otherwise obtained through storing multiple copies of the sparse data sorted on different permutations of the member keys of the multidimensional composite key (FIG. 2C). The data structure has two major components: the partitioned data with or without the boundary keys and the non-dense index necessarily containing the boundary keys (FIG. 3). This data structure and partitioning method can be used to partition multidimensional data across physical hardware storage devices and parallel computer architectures (FIG. 4) to produce variable grained data partitions that can be queried, loaded, or updated in parallel and simultaneous multidimensional index selectivity. And finally, as noted above, this same partitioning method can be used in a nested fashion or in combination with other data structures to provide good performance for the batch maintenance of volatile data and special performance advantages for true time-series data.

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First Claim