Abstract: A computer-implemented method for detecting reference data standardization gaps in data sets is disclosed. The method comprises identifying at least one reference data candidate in a data set, using an index for values of the identified at least one reference data candidate, and determining a difference between an earlier version of a reference data set relating to the reference data candidate and a current version of the reference data set. Furthermore, the method comprises comparing the determined difference with values of the index, and identifying entries in the at least one reference data candidate having a value identical to a value of the difference as reference data standardization gap.

Abstract: A method includes training a machine learning model using a current set of labeled data points. Each of the data points is multiple data records. A label of a data point indicates a classification of the data point. The training results in a trained machine learning model configured to classify a data point as representing a same entity or different entities. The method includes selecting a subset of unlabeled data points from a current set of unlabeled data points using classification results of the current set of unlabeled data points. The method includes providing the subset of unlabeled data points to a classifier and in response to providing receiving labels of the subset of unlabeled data points. The method may be repeated using the subset of labeled data points in addition to the current set of labeled data points as the current set of labeled data points.

Abstract: A method, computer system, and computer program product for training a machine learning model for use by a task management system are provided. The embodiment may include presenting a task to be resolved to a user via a user interface. The embodiment may also include presenting a further task to be resolved to the user via the user interface. The embodiment may further include predicting time to be spent on the further task presented to the user. The embodiment may also include determining actual time the user spent completing the further task. The embodiment may further include training a machine learning model for a subsequent similar task based on the predicted time and the determined actual time.

Abstract: A method includes training a machine learning model using a current set of labeled data points. Each of the data points is multiple data records. A label of a data point indicates a classification of the data point. The training results in a trained machine learning model configured to classify a data point as representing a same entity or different entities. The method includes selecting a subset of unlabeled data points from a current set of unlabeled data points using classification results of the current set of unlabeled data points. The method includes providing the subset of unlabeled data points to a classifier and in response to providing receiving labels of the subset of unlabeled data points. The method may be repeated using the subset of labeled data points in addition to the current set of labeled data points as the current set of labeled data points.

Abstract: An approach for accessing multi-attribute data records of a master data management system. The method comprises: enhancing the master data management system with one or more search engines for enabling data record access. A request of data may be received at the master data management system. A set of one or more of the multiple attributes, referenced in the received request, may be identified. A combination of one or more of the search engines of the master data management system, whose performances for searching values of at least part of the set of attributes fulfil a current selection rule may be selected. And, the request may be processed using the combination of search engines. At least part of the results of the processing may be provided, and the selection rule may be updated based on user operations on the provided results, the updated selection rule becoming the current selection rule.