Abstract: A method, system and computer program product for detecting business anomalies. Enterprise data as well as compliance rules (e.g., business operational rules) are analyzed to identify entities and concepts to be placed in an ontology. The identified entities and concepts are represented as multi-dimensional vectors. The updates, movements and access to the enterprise data and compliance rules are tracked to determine parameters, such as information velocity, associated with at least some of the elements of the multi-dimensional vectors. The meaning from the enterprise data as well as from the data parameters is discerned. The discerned enterprise data and discerned data parameters are compared with the historical and/or projected utilization of the discerned enterprise data and discerned data parameters to identify any differences. Statistical analysis is then applied based on the enterprise data and the identified differences to generate a value corresponding to a prediction of a business anomaly.

Abstract: A method, system and computer program product for detecting business anomalies. Enterprise data as well as compliance rules (e.g., business operational rules) are analyzed to identify entities and concepts to be placed in an ontology. The identified entities and concepts are represented as multi-dimensional vectors. The updates, movements and access to the enterprise data and compliance rules are tracked to determine parameters, such as information velocity, associated with at least some of the elements of the multi-dimensional vectors. The meaning from the enterprise data as well as from the data parameters is discerned. The discerned enterprise data and discerned data parameters are compared with the historical and/or projected utilization of the discerned enterprise data and discerned data parameters to identify any differences. Statistical analysis is then applied based on the enterprise data and the identified differences to generate a value corresponding to a prediction of a business anomaly.

Abstract: A method, system and computer program product for detecting business anomalies. Enterprise data as well as compliance rules (e.g., business operational rules) are analyzed to identify entities and concepts to be placed in an ontology. The identified entities and concepts are represented as multi-dimensional vectors. The updates, movements and access to the enterprise data and compliance rules are tracked to determine parameters, such as information velocity, associated with at least some of the elements of the multi-dimensional vectors. The meaning from the enterprise data as well as from the data parameters is discerned. The discerned enterprise data and discerned data parameters are compared with the historical and/or projected utilization of the discerned enterprise data and discerned data parameters to identify any differences. Statistical analysis is then applied based on the enterprise data and the identified differences to generate a value corresponding to a prediction of a business anomaly.

Abstract: A method, system and computer program product for detecting business anomalies. Enterprise data as well as compliance rules (e.g., business operational rules) are analyzed to identify entities and concepts to be placed in an ontology. The identified entities and concepts are represented as multi-dimensional vectors. The updates, movements and access to the enterprise data and compliance rules are tracked to determine parameters, such as information velocity, associated with at least some of the elements of the multi-dimensional vectors. The meaning from the enterprise data as well as from the data parameters is discerned. The discerned enterprise data and discerned data parameters are compared with the historical and/or projected utilization of the discerned enterprise data and discerned data parameters to identify any differences. Statistical analysis is then applied based on the enterprise data and the identified differences to generate a value corresponding to a prediction of a business anomaly.

Abstract: In a computer, a first set of object classes are provided representing active entities in an information-handling process and a second set of object classes are provided representing data and rules in the information-handling process. At least one object class has rules associated with data. The above-mentioned objects are used in constructing a model of an information-handling process, and to provide an output that identifies at least one way in which the information-handling process could be improved. One aspect is a method for handling personally identifiable information. Another aspect is a system for executing the method of the present invention. A third aspect is as a set of instructions on a computer-usable medium, or resident in a computer system, for executing the method of the present invention.

Abstract: The invention entails identifying the parties involved in a process of handling personally identifiable information; identifying the data involved in said process; classifying the data; expressing each relationship between each pair of said parties in terms of a privacy agreement; and representing the parties, data, and privacy agreements graphically in one or more privacy agreement relationship diagrams. The invention has the advantage of identifying opportunities to reduce privacy-related risks, including identifying unnecessary exchanges of data, for possible elimination, and identifying opportunities to transform data into a less sensitive form. Privacy agreements are based on a limited set of privacy-related actions: access, disclose, release, notify, utilize, update, withdrawConsent, giveConsent, delete, anonymize, depersonalize, and repersonalize. One aspect of the present invention is a method for improving the handling of personally identifiable information.

Abstract: The present invention is a system and method for handling personally identifiable information, using a rules model. The invention involves defining a limited number of privacy-related actions regarding personally identifiable information; constructing a rule for each circumstance in which one of said privacy-related actions may be taken or must be taken; allowing for the input of dynamic contextual information to precisely specify the condition for evaluation of a rule; creating a programming object containing at least one of said rules; associating the programming object with personally identifiable information; processing a request; and providing an output. The invention does not merely give a “yes-or-no answer. The invention has the advantage of being able to specify additional actions that must be taken. The invention may use a computer system and network. One aspect of the present invention is a method for handling personally identifiable information.

Abstract: The present invention is a system and method for handling personally identifiable information, using a rules model. The invention involves defining a limited number of privacy-related actions regarding personally identifiable information; constructing a rule for each circumstance in which one of said privacy-related actions may be taken or must be taken; allowing for the input of dynamic contextual information to precisely specify the condition for evaluation of a rule; creating a programming object containing at least one of said rules; associating the programming object with personally identifiable information; processing a request; and providing an output. The invention does not merely give a “yes-or-no answer. The invention has the advantage of being able to specify additional actions that must be taken. The invention may use a computer system and network. One aspect of the present invention is a method for handling personally identifiable information.

Abstract: The invention entails identifying the parties involved in a process of handling personally identifiable information; identifying the data involved in said process; classifying the data; expressing each relationship between each pair of said parties in terms of a privacy agreement; and representing the parties, data, and privacy agreements graphically in one or more privacy agreement relationship diagrams. The invention has the advantage of identifying opportunities to reduce privacy-related risks, including identifying unnecessary exchanges of data, for possible elimination, and identifying opportunities to transform data into a less sensitive form. Privacy agreements are based on a limited set of privacy-related actions: access, disclose, release, notify, utilize, update, withdrawConsent, giveConsent, delete, anonymize, depersonalize, and repersonalize. One aspect of the present invention is a method for improving the handling of personally identifiable information.

Abstract: The present invention involves providing in a computer a first set of object classes representing active entities in an information-handling process, wherein a limited number of privacy-related actions represent operations performed on data, and providing in the computer a second set of object classes representing data and rules in the information-handling process, wherein at least one object class has rules associated with data. The invention also involves using the above-mentioned objects in constructing a model of an information-handling process, and providing output that identifies at least one way in which the information-handling process could be improved. One aspect of the present invention is a method for handling personally identifiable information. Another aspect of the present invention is a system for executing the method of the present invention.