Abstract: An approach is provided for selecting one or more trust factors from trust factors included in a trust index repository. Thresholds are identified corresponding to one or more of the selected trust factors. Actions are identified to perform when the selected trust factors reach the corresponding threshold values. The identified thresholds, identified actions, and selected trust factors are stored in a data store. The selected trust factors are monitored by comparing one or more trust metadata scores with the stored identified thresholds. The stored identified actions that correspond to the selected trust factors are performed when one or more of the trust metadata scores reach the identified thresholds. At least one of the actions includes an event notification that is provided to a trust data consumer.

Abstract: An approach is provided for selecting a trust factor from trust factors that are included in a trust index repository. A trust metaphor is associated with the selected trust factor. The trust metaphor includes various context values. Range values are received and the trust metaphor, context values, and range values are associated with the selected trust factor. A request is received from a data consumer, the request corresponding to a trust factor metadata score that is associated with the selected trust factor. The trust factor metadata score is retrieved and matched with the range values. The matching results in one of the context values being selected based on the retrieved trust factor metadata score. The selected context value is then provided to the data consumer.

Abstract: An approach is provided in which atomic trust scores are computed using a atomic trust factors that are applied to a plurality of metadata. A first set of composite trust scores are computed using some of the atomic trust scores. The composite trust scores are computed using a first set of algorithms. Some of the algorithms use a factor weighting value as input to the algorithm. A second plurality of composite trust scores is computed using some of the composite trust scores that were included in the first set of scores as input. A fact and one of the second set of composite trust scores are presented to a user. The presented composite trust score provides a trustworthiness value that corresponds to the presented fact.

Abstract: Provided are a method, system, and article of manufacture for using a data mining algorithm to discover data rules. A data set including multiple records is processed to generate data rules for the data set. Each record has a record format including a plurality of fields and each rule provides a predicted condition for one field based on at least one predictor condition in at least one other field. The generated data rules are provided to a user interface to enable a user to edit the generated data rules. The data rules are stored in a rule repository to be available to use to validate data sets having the record format.

Abstract: An approach is provided for selecting one or more trust factors from trust factors included in a trust index repository. Thresholds are identified corresponding to one or more of the selected trust factors. Actions are identified to perform when the selected trust factors reach the corresponding threshold values. The identified thresholds, identified actions, and selected trust factors are stored in a data store. The selected trust factors are monitored by comparing one or more trust metadata scores with the stored identified thresholds. The stored identified actions that correspond to the selected trust factors are performed when one or more of the trust metadata scores reach the identified thresholds. At least one of the actions includes an event notification that is provided to a trust data consumer.

Abstract: An approach is provided for selecting a trust factor from trust factors that are included in a trust index repository. A trust metaphor is associated with the selected trust factor. The trust metaphor includes various context values. Range values are received and the trust metaphor, context values, and range values are associated with the selected trust factor. A request is received from a data consumer, the request corresponding to a trust factor metadata score that is associated with the selected trust factor. The trust factor metadata score is retrieved and matched with the range values. The matching results in one of the context values being selected based on the retrieved trust factor metadata score. The selected context value is then provided to the data consumer.

Abstract: An approach is provided in which atomic trust scores are computed using a atomic trust factors that are applied to a plurality of metadata. A first set of composite trust scores are computed using some of the atomic trust scores. The composite trust scores are computed using a first set of algorithms. Some of the algorithms use a factor weighting value as input to the algorithm. A second plurality of composite trust scores is computed using some of the composite trust scores that were included in the first set of scores as input. A fact and one of the second set of composite trust scores are presented to a user. The presented composite trust score provides a trustworthiness value that corresponds to the presented fact.

Abstract: An approach is provided in which facts are received and then one or more atomic fact trust analyses are performed on the facts. The atomic fact trust analyses result in various atomic trust factor scores. Composite trust analysis is performed using the atomic trust factor scores. The composite trust analyses result in composite trust factor scores. The atomic trust factor scores and the composite trust factor scores are stored in a trust index repository that is managed by a trust index framework. A request is then received for trusted data, the request being from an information consumer. The trust index framework then retrieves one of the composite trust factor scores from the trust index repository, with the retrieved composite trust factor score corresponding to the trusted data request, and this the retrieved composite trust factor score is provided to the information consumer.

Abstract: Provided are a method, system, and article of manufacture for using a data mining algorithm to discover data rules. A data set including multiple records is processed to generate data rules for the data set. Each record has a record format including a plurality of fields and each rule provides a predicted condition for one field based on at least one predictor condition in at least one other field. The generated data rules are provided to a user interface to enable a user to edit the generated data rules. The data rules are stored in a rule repository to be available to use to validate data sets having the record format.

Abstract: The present invention provides methods and systems for discovering and determining algebraic relationships between sets of data, such as numeric columns in a relational database, based on a “bottom-up” (or data-driven) approach. Embodiments of the present invention provide for the discovery and determination of algebraic relationships within a single relation or algebraic relationships across multiple tables that can be joined via a foreign key relationship. The foreign key relation can be one-to-one, many-to-one, or one-to-many. In order to discover algebraic relations, mean, variance and correlations calculations between columns are performed, for example, based on taking samples of the columns. Irreducible relations are then determined. Samples are taken from the irreducible relations and algebraic relationships between columns are determined based on various calculation techniques and correlations between the columns.