Abstract: An information integrity rules framework manages rules across heterogeneous enforcement systems by managing canonical rules that are mapped to native rules and enforcement system-specific rules. The information integrity rules framework discovers an existing native rule utilized by an enforcement system, which is written in an enforcement system-specific format. Next, the information integrity rules framework creates a mapping entry and maps the native rule to a canonical rule, which is independent from the enforcement system. When the information integrity rules framework detects a change to one of the rules managed by the framework, the information integrity rules framework propagates the change to other corresponding rules across enforcement systems managed by the framework.

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: Evaluating and prioritizing architectural design pattern alternatives for data integration is provided. A set of decision factors is derived from requirements for a system to be integrated. A default score is assigned to each decision factor based on historical data integration knowledge, and the default scores are weighted. A priority score is also assigned to each decision factor based on collected inputs from system metadata and subject matter experts in the enterprise system. Next, an individual consolidated score for each decision factor is calculated using the default score, the weighted score, and the priority score, and a total consolidated score is calculated from the individual consolidated scores for the architecture design pattern. The total consolidated score for the architecture design pattern may be compared against total consolidated scores for other architecture design patterns to determine a suitable candidate architecture design pattern for data integration.

Abstract: An information integrity rules framework manages rules across heterogeneous enforcement systems by managing canonical rules that are mapped to native rules and enforcement system-specific rules. The information integrity rules framework discovers an existing native rule utilized by an enforcement system, which is written in an enforcement system-specific format. Next, the information integrity rules framework creates a mapping entry and maps the native rule to a canonical rule, which is independent from the enforcement system. When the information integrity rules framework detects a change to one of the rules managed by the framework, the information integrity rules framework propagates the change to other corresponding rules across enforcement systems managed by the framework.

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 information integrity rules framework manages rules across heterogeneous enforcement systems by managing canonical rules that are mapped to native rules and enforcement system-specific rules. The information integrity rules framework discovers an existing native rule utilized by an enforcement system, which is written in an enforcement system-specific format. Next, the information integrity rules framework creates a mapping entry and maps the native rule to a canonical rule, which is independent from the enforcement system. When the information integrity rules framework detects a change to one of the rules managed by the framework, the information integrity rules framework propagates the change to other corresponding rules across enforcement systems managed by the framework.

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: 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 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.