Abstract: A computer-implemented method for obtaining a datasource schema comprising column-specific data-types and/or semantic-types from received tabular data records with values arranged in rows and columns, said method including: extracting a feature vector record comprising data-type recognition features for each of one or more columns of the received input tabular records; feeding the extracted feature vector records to a pretrained type classification discriminative machine learning model; using said model for classifying each extracted feature vector record of a corresponding column of received input tabular records into an estimated data-type and/or semantic-type, respectively, of the corresponding column. It is further disclosed a computer program product, a computer system and a method for training a machine learning model for obtaining the datasource schema.

Abstract: In an embodiment, a process for automated rules management system includes receiving a specification of past predicted results of evaluation rules and corresponding observed outcomes. The process includes determining one or more sets of alternative activations or priorities of at least a portion of the evaluation rules, assessing the one or more sets of alternative activations or priorities of at least a portion of the evaluation rules, and optimizing result activations or priorities of at least a portion of the evaluation rules based at least in part on the assessment of the one or more sets of alternative activations or priorities.

Abstract: In various embodiments, a process for providing an active learning annotation system that does not require historical data includes receiving a stream of unlabeled data, identifying a portion of the unlabeled data to label without access to label information, and receiving a labeled version of the identified portion of the unlabeled data and storing the labeled version as labeled data. The process includes analyzing the labeled version and at least a portion of the received unlabeled data that has not been labeled to identify an additional portion of the unlabeled data to label and store in the labeled data including by applying at least one warm up policy.

Abstract: In an embodiment, a process for automated rules management system includes receiving a specification of past predicted results of evaluation rules and corresponding observed outcomes. The process includes determining one or more sets of alternative activations or priorities of at least a portion of the evaluation rules, assessing the one or more sets of alternative activations or priorities of at least a portion of the evaluation rules, and optimizing result activations or priorities of at least a portion of the evaluation rules based at least in part on the assessment of the one or more sets of alternative activations or priorities.