There is provided a method for combining datasets, comprising: receiving a primary training dataset; receiving unclassified secondary dataset(s) comprising secondary data instances including secondary fields; identifying, for a first set of values of primary field(s) of training dataset, a second set of secondary fields of the secondary datasets according to the first set of values matched to corresponding values in respective secondary field(s) of secondary dataset(s) according to a matching requirement; linking each respective matched values to other secondary fields of the respective matched secondary field; generating a set of classification features based at least on the linked secondary fields; selecting a subset of pivotal classification features according to a correlation requirement; identifying a subset of pivotal secondary fields based on the secondary fields associated with each pivotal classification feature; and documenting the selected subset of pivotal features for use in an automated machine learning process.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method for combining datasets for use in an automated machine learning process, comprising: receiving a designation of a primary training dataset comprising a plurality of primary data instances, each primary data instance including a plurality of primary fields each assigned at least one value, each primary data instance associated with a classification label; receiving a designation of at least one secondary dataset each comprising a plurality of secondary data instances, each secondary data instance including a plurality of secondary fields, each secondary data being unclassified; identifying, for a first set of values of at least one primary field of the plurality of primary fields, a second set of secondary fields of the at least one secondary datasets according to the first set of values matched to corresponding values in at least one respective secondary field of at least one secondary dataset according to a matching requirement; linking each respective matched value of the first set of values, to other secondary fields of at least one respective secondary data instance of the respective matched secondary field; generating a set of classification features based at least on the linked second set of secondary fields, each classification feature for application to at least one field from the linked second set of secondary data fields; applying each classification feature of the generated set of classification features to each linked second set of secondary fields to generate a set of extracted features; selecting a subset of pivotal classification features from the set of classification features according to a correlation requirement between the classification label of the primary data instance corresponding to the linked secondary field used in the respective classification feature, and each respective member of the set of extracted features extracted by the respective classification feature; identifying a subset of pivotal secondary fields based on the secondary fields associated with each selected pivotal classification feature; creating an enhanced training dataset by linking the subset of pivotal secondary fields to the primary training dataset; and documenting at least one of the selected subset of pivotal features and enhanced training dataset for use in an automated machine learning process. 2. The method of claim 1, further comprising iterating the method until a stop condition is met, by designating the enhanced training dataset, and repeating the identifying the second set of secondary fields, linking, generating, applying, selecting, identifying the subset of pivotal secondary fields, and the creating, using the designated enhanced training dataset. 3. The method of claim 1, wherein at least some of the features extracted from at least one secondary field are statistically insignificantly correlated with the classification label. 4. The method of claim 1, wherein the matching requirement includes a percentage of the first set of values of the respective primary field matching the corresponding values in the respective secondary dataset. 5. The method of claim 1, wherein the matching requirement comprises that the first set of values is selected by sampling a subset of values of the at least one primary field. 6. The method of claim 1, further comprising preparing, for each secondary dataset, a set-representation of the plurality of secondary fields, and using the set-representation to match the first set of value and to identify the second set. 7. The method of claim 1, wherein at least one member of the at least one secondary dataset is selected from the group consisting of: a table wherein each row represents a secondary data instance and each column represents a secondary field and a graph comprising linked data. 8. The method of claim 1, wherein at least one primary field includes a third set of data elements each assigned a value, and wherein identifying comprises identifying for the first set of values of a subset including at least one data element of the third set. 9. The method of claim 1, wherein generating the set of classification features comprises generating at least one binary classification feature that extracts a binary value from at least one of the linked secondary data fields. 10. The method of claim 9, wherein the at least one binary classification feature includes at least one mathematical condition applied to at least one value of the linked secondary data fields of other data instances. 11. The method of claim 10, wherein the at least one mathematical condition is selected from the group consisting of: greater than, less than, equal to, greater than or equal to, less than or equal to, and containing the at least one value. 12. The method of claim 1, further comprising automatically designating at least one secondary dataset based at least one object type of at least one secondary field that corresponds to at least one object type of the primary training dataset. 13. The method of claim 1, further comprising analyzing at least one primary data field to extract at least one entity according to at least one object type, and wherein identifying comprises identifying the second set of secondary fields according to the object type of the first set of values of the at least one extracted entity. 14. The method of claim 1, further comprising converting values in at least one of: the primary training dataset and the at least one secondary data set to a canonical representation, and performing the identifying based on the canonical representation. 15. The method of claim 1, further comprising training a statistical classifier based on the selected subset of pivotal features applied to the enhanced training dataset, and the associated data classification labels. 16. The method of claim 1, further comprising classifying a new data instance to at least one of the data classification labels, by applying each classification feature of the selected subset of pivotal features to the new data instance to extract a plurality of features and applying the statistical classifier to the plurality of extracted features to output at least one of the data classification labels. 17. A system to create an enhanced training dataset for use in an automated machine learning process, comprising: a primary interface for communication with a first storage unit storing thereon a primary training dataset comprising a plurality of primary data instances, each primary data instance including a plurality of primary fields each assigned at least one value, each primary data instance associated with a classification label; a secondary interface for communication with at least one second storage unit storing thereon at least one secondary dataset each comprising a plurality of secondary data instances, each secondary data instance including a plurality of secondary fields, each secondary data being unclassified; a program store storing code; and a processor coupled to the primary interface, the secondary interface, and the program store for implementing the stored code, the code comprising: code to identify, for a first set of values of at least one primary field of the plurality of primary fields, a second set of secondary fields of the at least one secondary datasets according to the first set of values matched to corresponding values in at least one respective secondary field of at least one secondary dataset according to a matching requirement, to link each respective matched value of the first set of values, to other secondary fields of at least one respective secondary data instance of the respective matched secondary field, to generate a set of classification features based at least on the linked second set of secondary fields, each classification feature for application to at least one field from the linked second set of secondary data fields, to apply each classification feature of the generated set of classification features to each linked second set of secondary fields to generate a set of extracted features, to select a subset of pivotal classification features from the set of classification features according to a correlation requirement between the classification label of the primary data instance corresponding to the linked secondary field used in the respective classification feature, and each respective member of the set of extracted features extracted by the respective classification feature, to identify a subset of pivotal secondary fields based on the secondary fields associated with each selected pivotal classification feature, to create an enhanced training dataset by linking the subset of pivotal secondary fields to the primary training dataset; and to store at least one of the selected subset of pivotal features and enhanced training dataset for use in an automated machine learning process. 18. The system of claim 17, further comprising a physical user interface coupled to the processor, the user interface set to allow a user to designate the primary training set and the at least one secondary dataset. 19. The system of claim 17, wherein at least one secondary dataset is a publicly accessible database residing on a remote server, accessible over a network. 20. The system of claim 17, wherein at least one secondary dataset is retrieved from content published by a website. 21. The system of claim 17, wherein the matching requirement is based a maximum number of links and a processing time for creating the enhanced training dataset using a target processing unit. 22. The system of claim 17, further comprising code that automatically crawls along at least one of links on a network and stored file in the at least one second storage unit, the code automatically designates the at least one secondary dataset according to at least one object type that corresponds to at least one object type of the primary training dataset. 23. A computer program product comprising a non-transitory computer readable storage medium storing program code thereon for implementation by a processor of a system for creating enhanced training datasets for use in an automated machine learning process, the program code comprising: instructions to receive a designation of a primary training dataset comprising a plurality of primary data instances, each primary data instance including a plurality of primary fields each assigned at least one value, each primary data instance associated with a classification label; instructions to receive a designation of at least one secondary dataset each comprising a plurality of secondary data instances, each secondary data instance including a plurality of secondary fields, each secondary data being unclassified; instructions to identify, for a first set of values of at least one primary field of the plurality of primary fields, a second set of secondary fields of the at least one secondary datasets according to the first set of values matched to corresponding values in at least one respective secondary field of at least one secondary dataset according to a matching requirement; instructions to link each respective matched value of the first set of values, to other secondary fields of at least one respective secondary data instance of the respective matched secondary field; instructions to generate a set of classification features based at least on the linked second set of secondary fields, each classification feature for application to at least one field from the linked second set of secondary data fields; instructions to apply each classification feature of the generated set of classification features to each linked second set of secondary fields to generate a set of extracted features; instructions to select a subset of pivotal classification features from the set of classification features according to a correlation requirement between the classification label of the primary data instance corresponding to the linked secondary field used in the respective classification feature, and each respective member of the set of extracted features extracted by the respective classification feature; instructions to identify a subset of pivotal secondary fields based on the secondary fields associated with each selected pivotal classification feature; instructions to create an enhanced training dataset by linking the subset of pivotal secondary fields to the primary training dataset; and instructions to document at least one of the selected subset of pivotal features and enhanced training dataset for use in an automated machine learning process.