Certain example embodiments described herein relate to techniques for managing “bad” or “imperfect” data being imported into a database system. That is, certain example embodiments provide a lifecycle technology solution that helps receive data from a variety of different data sources of a variety of known and/or unknown formats, standardize it, fit it to a known taxonomy through model-assisted classification, store it to a database in a manner that is consistent with the taxonomy, and allow it to be queried for a variety of different usages. Some or all of the disclosed technology concerning auto-classification, enrichment, clustering model and model stacks, and/or the like, may be used in these and/or other regards.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A data classification system, comprising: an input interface configured to receive documents comprising line-item data entries, at least some of the line-item data entries having associated attributes represented directly in the documents; a data warehouse backed by a non-transitory computer readable storage medium and configured to store curated and classified data elements; a classification model stack including (a) a plurality of classification models, (b) a plurality of confidence models, and (c) a related multi-level taxonomy of classifications applicable to line-item data entries included in documents received via the input interface; and processing resources including at least one processor and a memory, the processing resources being configured to at least: execute classification models from the classification model stack to associate the line-item data entries included in the documents received via the input interface with potential classifications at each level in the related taxonomy; execute confidence models from the classification model stack to assign probabilities of correctness for each potential classification generated by execution of the classification models; determine, for each of the line-item data entries included in the documents received via the input interface, a most granular level of potential classification that meets or exceeds a threshold value; designate a classification result corresponding to the determined most granular level of potential classification for each of the line-item data entries included in the documents received via the input interface; store each line-item data entry, with or without additional processing, to the data warehouse, along with an indication of its associated classification result; and enable records in the data warehouse to be queried from a computer terminal. 2. The system of claim 1, wherein each of the classification models is a vector space model (VSM) and each of the confidence models is a Bayesian generalized linear classification model (BGLM). 3. The system of claim 1, wherein at least one of the confidence models involves voting to resolve disagreements between potential classifications derived by execution of the classification models. 4. The system of claim 3, wherein the at least one of the confidence model that involves voting is practiced using simple majority-rules voting on potential classifications made at each level of the related taxonomy for each of the line-item data entries included in the documents received via the input interface. 5. The system of claim 3, wherein the at least one of the confidence model that involves voting is practiced using proportional voting on potential classifications for each of the line-item data entries included in the documents received via the input interface. 6. The system of claim 5, wherein the proportional voting weights more granular potential classifications more heavily than less granular potential classifications. 7. The system of claim 1, wherein at least some of the classification models from the classification model stack associate each of the line-item data entries included in the documents received via the input interface with multiple potential classifications at each level in the related taxonomy. 8. The system of claim 7, wherein at least one of the confidence models involves using proportional voting on the multiple potential classifications for each of the line-item data entries included in the documents received via the input interface to resolve disagreements between potential classifications derived by execution of the classification models. 9. The system of claim 8, wherein the proportional voting weights potential classifications based on their ordering such that each successive potential classification is weighted less than the preceding potential classification. 10. The system of claim 1, wherein the confidence models comprise a family of different Bayesian generalized linear classification models. 11. The system of claim 1, wherein the processing resources are further configured to at least confine dimensionality of a classification space in which the classification models operate by executing a series of progressively narrower classifications. 12. The system of claim 11, wherein the series of progressively narrower classifications includes an executable deterministic category model and/or an executable natural language processing noun modifier model, followed by a vector space model. 13. The system of claim 1, wherein: each level in the related taxonomy is associated with a set of weights relative to a learned dictionary of words from a training dataset, such that each category in each level in the related taxonomy is represented as a vector within an n-dimensional (N-D) vector space, where n is the number of words in the learned dictionary; and at least one of the classification models is configured to, for each line-item data entry: represent the respective line-item data entry as a vector in the N-D vector space; identify a predetermined number of nearest neighbors to the vector, using angles as distance measures between the vector and neighbors; and associate the respective line-item data entry with the potential classification corresponding to one or more of the identified nearest neighbors; and N-D category vectors are learned a priori from the training data set. 14. The system of claim 13, wherein at least one of the confidence models is configured to use measures from the at least one classification model and/or system-provided information about the related taxonomy as predictors. 15. The system of claim 13, wherein angles are used as distance measures by taking the dot products of the vector and the neighbors and dividing by the product of the respective Euclidean norms. 16. The system of claim 1, wherein at least one of the classification models is self-learning. 17. The system of claim 16, wherein the self-learning occurs with successive classifications. 18. The system of claim 16, wherein the self-learning occurs by treating the attributes associated with successive classifications as further reference data for use in a training dataset. 19. A method of data classification, the method comprising: receiving, over an input interface, documents comprising line-item data entries, at least some of the line-item data entries having associated attributes represented directly in the documents; having a classification model stack including (a) a plurality of classification models, (b) a plurality of confidence models, and (c) a related multi-level taxonomy of classifications applicable to line-item data entries included in documents received via the input interface; and executing, using processing resources including at least one processor and a memory, classification models from the classification model stack to associate the line-item data entries included in the documents received via the input interface with potential classifications at each level in the related taxonomy; executing, using the processing resources, confidence models from the classification model stack to assign probabilities of correctness for each potential classification generated by execution of the classification models; determining, for each of the line-item data entries included in the documents received via the input interface, a most granular level of potential classification that meets or exceeds a threshold value; designating a classification result corresponding to the determined most granular level of potential classification for each of the line-item data entries included in the documents received via the input interface; storing each line-item data entry, with or without additional processing, to a data warehouse, along with an indication of its associated classification result, the data warehouse being backed by a non-transitory computer readable storage medium and being configured to store curated and classified data elements; and enabling records in the data warehouse to be queried from a computer terminal. 20. A non-transitory computer-readable storage medium including instructions that, when executed by processing resources including at least one processor and a memory, are configured to at least: receive, over an input interface, documents comprising line-item data entries, at least some of the line-item data entries having associated attributes represented directly in the documents; execute a classification model stack that includes (a) a plurality of classification models, (b) a plurality of confidence models, and (c) a related multi-level taxonomy of classifications applicable to line-item data entries included in documents received via the input interface, by: executing classification models from the classification model stack to associate the line-item data entries included in the documents received via the input interface with potential classifications at each level in the related taxonomy; and executing confidence models from the classification model stack to assign probabilities of correctness for each potential classification generated by execution of the classification models; determine, for each of the line-item data entries included in the documents received via the input interface, a most granular level of potential classification that meets or exceeds a threshold value; designate a classification result corresponding to the determined most granular level of potential classification for each of the line-item data entries included in the documents received via the input interface; store each line-item data entry, with or without additional processing, to a data warehouse, along with an indication of its associated classification result, the data warehouse being backed by a non-transitory computer readable storage medium and being configured to store curated and classified data elements; and enable records in the data warehouse to be queried from a computer terminal. 21. The non-transitory computer-readable storage medium of claim 20, wherein each of the classification models is a vector space model (VSM) and each of the confidence models is a Bayesian generalized linear classification model (BGLM). 22. The non-transitory computer-readable storage medium of claim 20, wherein at least one of the confidence models involves voting to resolve disagreements between potential classifications derived by execution of the classification models. 23. The non-transitory computer-readable storage medium of claim 22, wherein the at least one of the confidence model that involves voting is practiced using (i) simple majority-rules voting on potential classifications made at each level of the related taxonomy for each of the line-item data entries included in the documents received via the input interface, or (ii) proportional voting on potential classifications for each of the line-item data entries included in the documents received via the input interface. 24. The non-transitory computer-readable storage medium of claim 20, wherein at least some of the classification models from the classification model stack associate each of the line-item data entries included in the documents received via the input interface with multiple potential classifications at each level in the related taxonomy. 25. The non-transitory computer-readable storage medium of claim 24, wherein at least one of the confidence models involves using proportional voting on the multiple potential classifications for each of the line-item data entries included in the documents received via the input interface to resolve disagreements between potential classifications derived by execution of the classification models. 26. The non-transitory computer-readable storage medium of claim 20, wherein the confidence models comprise a family of different Bayesian generalized linear classification models. 27. The non-transitory computer-readable storage medium of claim 20, wherein the dimensionality of a classification space in which the classification models operate is confined by executing a series of progressively narrower classifications that include: an executable deterministic category model and/or an executable natural language processing noun modifier model, followed by a vector space model. 28. The non-transitory computer-readable storage medium of claim 20, wherein at least one of the classification models is self-learning. 29. The non-transitory computer-readable storage medium of claim 28, wherein the self-learning occurs with successive classifications. 30. The non-transitory computer-readable storage medium of claim 28, wherein the self-learning occurs by treating the attributes associated with successive classifications as further reference data for use in a training dataset.