Disclosed herein are systems and methods for data learning and classification for rapidly processing extremely large volumes of input data using one or more computing devices, that are application and platform independent, participating in a distributed parallel processing environment. In one embodiments, a system may comprise a plurality of parallel Map Reduction Aggregation Processors operating on the one or more computing devices, and configured to receive different sets of input data for data aggregation. Each of the Map Reduction Aggregation Processors may comprise one or more parallel Mapping Operation Modules configured to consistently dissect the input data into individual intermediate units of mapping outputs comprising consistently mapped data keys, and any values related to mapped data keys, conducive to simultaneous parallel reduction processing; and one or more parallel Reduction Operation Modules configured to continually and simultaneously consume the mapping outputs by eliminating the matching keys and aggregating values consistent with a specified reduction operation for all matching keys that are encountered during consumption of the mapping outputs. The system may also include an application-specific Classification Metric Function Operations Module operating on the one or more computing devices and configured to receive reduction outputs from the Reduction Operations Modules to determine distance and/or similarity between each of the different sets of input data with respect to one or more data classification categories using one or more distance and/or similarity calculations.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A data learning and classification system for rapidly processing extremely large volumes of input data using one or more computing devices, that are application and platform independent, participating in a distributed parallel processing environment, the system comprising: a plurality of parallel Map Reduction Aggregation Processors operating on the one or more computing devices, and configured to receive different sets of input data for data aggregation, each of the Map Reduction Aggregation Processors comprising: one or more parallel Mapping Operation Modules configured to consistently dissect the input data into individual intermediate units of mapping outputs comprising consistently mapped data keys, and any values related to mapped data keys, conducive to simultaneous parallel reduction processing, and one or more parallel Reduction Operation Modules configured to continually and simultaneously consume the mapping outputs by eliminating the matching keys and aggregating values consistent with a specified reduction operation for all matching keys that are encountered during consumption of the mapping outputs; and an application-specific Classification Metric Function Operations Module operating on the one or more computing devices and configured to receive reduction outputs from the Reduction Operations Modules to determine distance and/or similarity between each of the different sets of input data with respect to one or more data classification categories using one or more distance and/or similarity calculations. 2. A data learning and classification system in accordance with claim 1, wherein the one or data classification categories comprise one or more parallel Nested Categorical Key Value Pair Collections consolidated from Reduction Operations Module outputs to provide key-based identifying of sets of input data. 3. A data learning and classification system in accordance with claim 2, wherein the key-based identifying is based on a category name associated with a category ID or a category ID associated with a category name. 4. A data learning and classification system in accordance with claim 3, further comprising a Category Manager comprising at least one key value pair collection of unique category names and category IDs, wherein the Map Reduction Aggregation Processors are further configured to access the Category Manager to provide the key-based identifying of input data using known category names and category IDs. 5. A data learning and classification system in accordance with claim 4, wherein the collection of categorical key value pairs for each key provides a categorical association including an optional numerical description of that key across any number of categorical keys contained in a Nested Categorical Key Value Pair Collection. 6. A data learning and classification system in accordance with claim 4, wherein the one or more Nested Categorical Key Value Pair Collections comprise all outputs using similar map reduction methods in at least one collection of nested key value pairs where the key for each key value pair maps to a nested collection of categorical key value pairs as its value. 7. A data learning and classification system in accordance with claim 4, wherein the Category Manager further comprises a Default Frequencies Collection comprising default category associations which may also include default starting frequencies for input data when Reduction Operations Module outputs contain one or more key values for input into the Nested Categorical Key Value Pair Collection. 8. A data learning and classification system in accordance with claim 7, wherein when unknown key values are encountered in Reduction Operations Module outputs during map reduction aggregation consolidation, the Default Frequencies Collection is accessed and at least one default category which may also include a default frequency for the input data being processed is used as a starting nested categorical key value pair entry collection in the new nested key value pair entry. 9. A data learning and classification system in accordance with claim 4, wherein the Classification Metric Function Operations Module may be configured to use a Classification Total Collection, comprising totals for each matching category identified when the Reduction Operations Module outputs contain keys that currently exist in the Nested Categorical Key Value Pair Collection, and to use a Category Totals Collection, comprising representative totals for each category, to produce a collection of penetration totals representing a classification score for each category identified during classification processing by the Classification Metric Function Module. 10. A data learning and classification system in accordance with claim 1, further comprising a blocking mechanism configured to regulate Mapping Operations Module outputs when production capacity exceeds a pre-determined production capacity threshold. 11. A data learning and classification system in accordance with claim 1, wherein each Map Reduction Aggregation Module further comprises one or more parallel Locality Sensitive Hashing (LSH) Operations Modules, each configured to produce a collection of values representing unique characteristics of combined Reduction Operation Module outputs from a specific set of input data, wherein the number of values contained within LSH Operations Module outputs for each set of input data is based on a number of distinct hash functions performed by each LSH Operations Module. 12. A data learning and classification system in accordance with claim 11, wherein each Map Reduction Aggregation Module further comprises a Second Blocking Mechanism configured to regulate Reduction Operations Module outputs when production capacity exceeds a pre-determined production capacity threshold. 13. A data learning and classification system in accordance with claim 11, wherein the Classification Metric Function Operations Module is further configured to receive LSH Operations Module outputs for use in the determining of similarity and/or distance between the different sets of input data and categories. 14. A data learning and classification system in accordance with claim 11, further comprising one or more parallel Nested Categorical Key Value Pair Collections consolidated from Reduction Operations Module outputs to provide key-based identifying of input data by category name associated with a category ID or category ID associated with category name, wherein the one or more Nested Categorical Key Value Pair Collections are further configured to store LSH Operations Module outputs prior to receipt by the Classification Metric Function Operations Module. 15. A data learning and classification system in accordance with claim 11, wherein the one or more LSH Operations Modules are further configured to produce one or more resulting Minimum Hash (MinHash) Values for each of the distinct hash functions performed, wherein a Collection of MinHash Value represents the unique characteristics of the sets of input data. 16. A data learning and classification system in accordance with claim 15, wherein the Map Reduction Aggregation Processors are further configured to consolidate the MinHash Values by storing LSH Operations Module outputs in one or more collections of nested key value pairs where the key for each key-value pair maps to a Nested Collection of Categorical Key-Value Pairs as its value. 17. A data learning and classification system in accordance with claim 16, wherein the Nested Collection of Categorical Key-Value Pairs may be partitioned based on each distinct hash function by assigning each distinct hash function a distinct hash function ID. 18. A data learning and classification system in accordance with claim 1, wherein the plurality of Map Reduction Aggregation Processors is configured to receive parallel sets of input data simultaneously. 19. A data learning and classification system in accordance with claim 1, wherein the Classification Metric Function Operations Module determines distance and/or similarity between the different sets of input data and categories using a classification metric function specified by an application programmer. 20. A data aggregation system in accordance with claim 19, wherein the Classification Metric Function Operations Module determine distance and/or similarity between the different sets of input data by performing multiple distance and/or similarity metric calculations consistent with the specified classification metric function.