Systems, methods, and other embodiments are disclosed for partitioning models in a database. In one embodiment, a set of training data is parsed into multiple data partitions based on partition keys, where the data partitions are identified by the partition keys and are used for training data mining models. The multiple data partitions are analyzed to generate partition metrics data. Algorithm data, identifying at least one algorithm for processing the multiple data partitions, and resources data, identifying available modeling resources for processing the multiple data partitions, are read. The partition metrics data, the algorithm data, and the resources data are processed to generate an organization data structure. The organization data structure is configured to control distribution and processing of the multiple data partitions across the available modeling resources to generate a composite model object that includes a separately trained data mining model for each partition of the multiple partitions.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method performed by at least a processor for executing instructions from a memory, the method comprising: parsing a set of training data, via the processor within a relational database system having the processor and the memory, into a plurality of data partitions based on partition keys, wherein the plurality of data partitions are identified by the partition keys and are used for training data mining models; analyzing the plurality of data partitions, via the processor, to generate partition metrics data, wherein the partition metrics data include at least a number of partitions of the plurality of data partitions, a size of each partition of the plurality of data partitions, and a respective value for a partition key of the partition keys associated with each partition of the plurality of data partitions; reading, via the processor, algorithm data identifying at least one algorithm for processing the plurality of data partitions, and resources data identifying available modeling resources for processing the plurality of data partitions; and analyzing, via the processor, the partition metrics data, the algorithm data, and the resources data to adaptively generate an organization data structure as the resources data changes at runtime, wherein the organization data structure is configured to control distribution and processing of the plurality of data partitions across the available modeling resources to generate a composite model object that includes a separately trained data mining model for each partition of the plurality of data partitions. 2. The method of claim 1, wherein the resources data, identifying available modeling resources, includes data identifying a plurality of internal processing nodes of the relational database system. 3. The method of claim 1, wherein the resources data, identifying available modeling resources, includes data identifying a plurality of external processing nodes accessible by the relational database system via computerized network communications. 4. The method of claim 1, further comprising: distributing, via the processor, a first partition of the plurality of data partitions across a plurality of processing nodes of the available modeling resources as controlled by the organization data structure; processing the first partition in parallel across the plurality of processing nodes of the available modeling resources, as controlled by the organization data structure, to train a first data mining model of the composite model object; distributing, via the processor, a second partition of the plurality of data partitions across the plurality of processing nodes of the available modeling resources as controlled by the organization data structure; and processing the second partition in parallel across the plurality of processing nodes of the available modeling resources, as controlled by the organization data structure, to train a second data mining model of the composite model object. 5. The method of claim 1, further comprising: distributing, via the processor, a first partition of the plurality of data partitions to a first processing node of the available modeling resources as controlled by the organization data structure; distributing, via the processor, a second partition of the plurality of data partitions to a second processing node of the available modeling resources as controlled by the organization data structure; and processing the first and second partitions concurrently on the first and second processing nodes, respectively, as controlled by the organization data structure, to respectively train a first data mining model and a second data mining model of the composite model object. 6. The method of claim 1, further comprising: distributing, via the processor, a first partition of the plurality of data partitions to a first processing node of the available modeling resources as controlled by the organization data structure; distributing, via the processor, a second partition of the plurality of data partitions to a second processing node of the available modeling resources as controlled by the organization data structure; processing the first and second, partitions concurrently on the first and second processing nodes, respectively, as controlled by the organization data structure, to respectively train a first data mining model and a second data mining model of the composite model object; distributing, via the processor, a third partition of the plurality of data partitions across a plurality of processing nodes of the available modeling resources as controlled by the organization data structure; and processing the third partition in parallel across the plurality of processing nodes of the available modeling resources, as controlled by the organization data structure, to train a third data mining model of the composite model object. 7. The method of claim 1, further comprising specifying one or more partitioning data fields, wherein the one or more partitioning data fields specify types of data upon which to partition the training data, via one of: a graphical user interface; an application program interface; automatic determination based on data fields configured to physically partition the training data; or automatic determination based on an analysis of a content of the training data, and wherein the one or more partitioning data fields contain the partition keys. 8. The method of claim 1, further comprising generating the composite model object by distributing and processing the plurality of data partitions across the available modeling resources as controlled by the organization data structure. 9. The method of claim 8, further comprising adding or removing at least one data mining model from the composite model object. 10. The method of claim 8, further comprising replacing one or more data mining models within the composite model object by training one or more new data mining models for corresponding one or more of the partition keys based on new training data. 11. The method of claim 8, further comprising persisting the composite model object within a persistent data cache of the relational database system. 12. The method of claim 11, further comprising: parsing a set of scoring data within the relational database system, via the processor, into at least two data partitions based on at least two data partition keys, wherein the set of scoring data includes the at least two data partitions, and wherein the at least two data partitions are identified by the at least two partition keys and are loaded into the memory of the relational database system; loading a corresponding data mining model of the composite model object, for each partition of the at least two data partitions, from the persistent data cache of the relational database system into the memory of the relational database system based on the at least two data partition keys; and scoring each partition of the at least two data partitions by applying the corresponding data mining model to each partition of the at least two data partitions within the relational database system. 13. The method of claim 11, further comprising: loading, via the processor, at least two data partitions of a set of scoring data into the memory of the relational database system based on a physical partitioning of an underlying data source of the set of scoring data; loading corresponding data mining models of the composite model object, for each partition of the at least two data partitions, from the persistent data cache of the relational database system into the memory of the relational database system, wherein the physical partitioning of the underlying data source matches a partitioning of the composite model object; and scoring each partition of the at least two data partitions by applying the corresponding data mining models to corresponding partitions of the at least two data partitions within the relational database system. 14. A relational database system, comprising: a processor; a memory; a parsing module stored in a non-transitory computer-readable medium including instructions that when executed cause the processor to: read a set of training data within the relational database system, wherein the set of training data is identified by partition keys and is used for training data mining models, and parse the set of training data into a plurality of data partitions based on the partition keys; a metrics module stored in the non-transitory computer-readable medium including instructions that when executed cause the processor to analyze the plurality of data partitions to generate partition metrics data, wherein the partition metrics data include at least a number of partitions of the plurality of data partitions, a size of each partition of the plurality of data partitions, and a respective value for a partition key of the partition keys associated with each partition of the plurality of data partitions; and an organizing module stored in a non-transitory computer-readable medium including instructions that when executed cause the processor to: read the partition metrics data, algorithm data identifying at least one algorithm for processing the plurality of data partitions, and resources data identifying available modeling resources for processing the plurality of data partitions, and analyze the partition metrics data, the algorithm data, and the resources data to generate an organization data structure, wherein the organization data structure is configured to control distribution and processing of the plurality of data partitions across the available modeling resources to generate a composite model object that includes a separately trained data mining model for each partition of the plurality of data partitions. 15. The relational database system of claim 14, further comprising a scoring module stored in the non-transitory computer-readable medium including instructions that when executed cause the processor to: access a set of scoring data within the relational database system, wherein the set of scoring data includes at least two data partitions identified by at least two partition keys, and wherein the at least two data partitions are loaded into the memory of the relational database system; parse the set of scoring data into the at least two data partitions based on the at least two partition keys; load corresponding data mining models of the composite model object into the memory of the relational database system based on the at least two partition keys; and score each partition of the at least two data partitions by applying the corresponding data mining models to corresponding partitions of the at least two data partitions. 16. The relational database system of claim 14, further comprising a visual user interface module stored in the non-transitory computer-readable medium including instructions that when executed cause the processor to provide a graphical user interface or an application program interface that facilitates: user selection of one or more data fields upon which the training data is partitioned; and user adding and removing of partitions from the plurality of data partitions. 17. The relational database system of claim 14, further comprising: a persistent data cache; and the available modeling resources, wherein the available modeling resources include a plurality of processing nodes configured to: generate the composite model object at least in part by executing the at least one algorithm on the plurality of data partitions as controlled by the organization data structure; and persist the composite model object in the persistent data cache. 18. The relational database system of claim 17, further comprising a distributing module stored in a non-transitory computer-readable medium including instructions that when executed cause the processor to: read the organization data structure; and distribute data slices of a first partition of the plurality of data partitions across the plurality of processing nodes of the available modeling resources as controlled by the organization data structure. 19. The relational database system of claim 17, further comprising a distributing module stored in a non-transitory computer-readable medium including instructions that when executed cause the processor to: read the organization data structure; and distribute each partition of the plurality of data partitions to a different processing node of the plurality of processing nodes of the available modeling resources as controlled by the organization data structure. 20. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors of a computing device, cause the computing device to at least: parse a set of training data, via the one or more processors, into a plurality of data partitions based on partition keys, wherein the plurality of data partitions are identified by the partition keys and are used for training data mining models; analyze the plurality of data partitions, via the one or more processors, to generate partition metrics data, wherein the partition metrics data include at least a number of partitions, a size of each partition, and a respective value for a partition key of the partition keys associated with each partition of the plurality of data partitions; and generate an organization data structure by analyzing, via the one or more processors, the partition metrics data, algorithm data identifying at least one algorithm for processing the plurality of data partitions, and resources data identifying available modeling resources for processing the plurality of data partitions, wherein the organization data structure is configured to control distribution and processing of the plurality of data partitions across the available modeling resources to generate a composite model object that includes a separately trained data mining model for each partition of the plurality of data partitions. 21. The non-transitory computer-readable medium of claim 20, wherein the instructions further comprise instructions that, when executed by the one or more processors, cause the computing device to at least generate the composite model object by distributing and processing the plurality of data partitions across the available modeling resources as controlled by the organization data structure. 22. The non-transitory computer-readable medium of claim 20, wherein the instructions further comprise instructions that, when executed by the one or more processors, cause the computing device to at least: parse a set of scoring data into at least two data partitions based on at least two data partition keys, wherein the set of scoring data includes the at least two data partitions identified by the at least two data partition keys, and wherein the at least two data partitions are loaded into a memory of a relational database system; load corresponding data mining models of the composite model object into the memory of the relational database system based on the at least two data partition keys; and score each partition of the at least two data partitions by applying the corresponding data mining models to corresponding partitions of the at least two data partitions. 23. A computer-implemented method performed by at least a processor for executing instructions from a memory, the method comprising: reading, via the processor, a first row of scoring data from a set of scoring data, wherein the processor is part of a relational data base system, and wherein the relational database system includes the memory; parsing, via the processor, the first row of scoring data to identify the first partition key; checking, via the processor, whether a first data mining model of a composite model object corresponding to the first partition key is already loaded into the memory of the relational database system; in response to the checking, loading the first data mining model, via the processor, into the memory of the relational database system based on the first partition key when the first data mining model is not already loaded; scoring, via the processor, the first row of scoring data by applying the corresponding first data mining model to the first row of scoring data; continuing, via the processor, reading and parsing other rows of scoring data of the set of scoring data and checking for and loading corresponding other data mining models of the composite model object into the memory of the relational database system based on other partition keys of the other rows, and scoring the other rows by applying the corresponding data mining models to the other rows; and as the other data mining models are being loaded into the memory of the relational database system, remove the first data mining model from the memory of the relational database system when a specified age-out criterion is met, indicating a lack of application of the first data mining model to the other rows.