Abstract: Disclosed embodiments are related to Master Data Management (MDM) technologies. Each DB entity (record) in component databases (DB) is represented as a judgment, and an MDM system unifies judgments obtained from the component DBs into a unified set of judgments. In the unified set of judgments, linkages are judgments asserting that particular DB entities from different DBs are the same/similar, and a golden record comprises field values describing each of the DB entities. In making judgments, the MDM system consolidates judgments (or records) describing the same subject into a single judgment, and performs updates to field values in a manner that does not violate referential integrity. Each update is associated with an MDM consistent state. Updates in the form of judgments are provided to the relevant component DBs, which are converted into serializable transactions associated with respective MDM consistent states. Other embodiments may be described and/or claimed.

Abstract: Disclosed embodiments are related to Master Data Management (MDM) technologies. Each DB entity (record) in component databases (DB) is represented as a judgment, and an MDM system unifies judgments obtained from the component DBs into a unified set of judgments. In the unified set of judgments, linkages are judgments asserting that particular DB entities from different DBs are the same/similar, and a golden record comprises field values describing each of the DB entities. In making judgments, the MDM system consolidates judgments (or records) describing the same subject into a single judgment, and performs updates to field values in a manner that does not violate referential integrity. Each update is associated with an MDM consistent state. Updates in the form of judgments are provided to the relevant component DBs, which are converted into serializable transactions associated with respective MDM consistent states. Other embodiments may be described and/or claimed.

Abstract: The technology disclosed relates to methods for partitioning sets of features for a Bayesian classifier, finding a data partition that makes the classification process faster and more accurate, while discovering and taking into account feature dependence among sets of features in the data set. It relates to computing class entropy scores for a class label across all tuples that share the feature-subset and arranging the tuples in order of non-decreasing entropy scores for the class label, and constructing a data partition that offers the highest improvement in predictive accuracy for the data set. Also disclosed is a method for partitioning a complete set of records of features in a batch computation, computing increasing predictive power; and also relates to starting with singleton partitions, and using an iterative process to construct a data partition that offers the highest improvement in predictive accuracy for the data set.

Abstract: The technology disclosed relates to methods for partitioning sets of features for a Bayesian classifier, finding a data partition that makes the classification process faster and more accurate, while discovering and taking into account feature dependence among sets of features in the data set. It relates to computing class entropy scores for a class label across all tuples that share the feature-subset and arranging the tuples in order of non-decreasing entropy scores for the class label, and constructing a data partition that offers the highest improvement in predictive accuracy for the data set. Also disclosed is a method for partitioning a complete set of records of features in a batch computation, computing increasing predictive power; and also relates to starting with singleton partitions, and using an iterative process to construct a data partition that offers the highest improvement in predictive accuracy for the data set.