Abstract: Operations, such as data processing operations, can be improved by applying clustering and statistical techniques to observed behaviors in the data processing operations.

Abstract: Methods for analyzing and rendering business intelligence data allow for efficient scalability as datasets grow in size. Human intervention is minimized by augmented decision making ability in selecting what aspects of large datasets should be focused on to drive key business outcomes. Variable value combinations that are predominant drivers of key observations are automatically determined from several competing variable value combinations. The identified variable value combinations can then be then used to predict future trends underlying the business intelligence data. In another embodiment, an observed outcome is decomposed into multiple contributing drivers and the impact of each of the contributing drivers can be analyzed and numerically quantified—as a static snapshot or as a time-varying evolution.

Abstract: Methods for analyzing and rendering business intelligence data allow for efficient scalability as datasets grow in size. Human intervention is minimized by augmented decision making ability in selecting what aspects of large datasets should be focused on to drive key business outcomes. Variable value combinations that are predominant drivers of key observations are automatically determined from several competing variable value combinations. The identified variable value combinations can then be then used to predict future trends underlying the business intelligence data. In another embodiment, an observed outcome is decomposed into multiple contributing drivers and the impact of each of the contributing drivers can be analyzed and numerically quantified—as a static snapshot or as a time-varying evolution.

Abstract: Methods for analyzing and rendering business intelligence data allow for efficient scalability as datasets grow in size. Human intervention is minimized by augmented decision making ability in selecting what aspects of large datasets should be focused on to drive key business outcomes. Variable value combinations that are predominant drivers of key observations are automatically determined from several competing variable value combinations. The identified variable value combinations can then be then used to predict future trends underlying the business intelligence data. In another embodiment, an observed outcome is decomposed into multiple contributing drivers and the impact of each of the contributing drivers can be analyzed and numerically quantified—as a static snapshot or as a time-varying evolution.

Abstract: A combined computer/human approach is used to detect actionable insights in large data sets. Automated computer analysis used to identify patterns (e.g., possibly meaningful patterns or subsets within the data). These are presented to humans for feedback, where the humans may have little to no training in the statistical methods used to detect actionable insights. Feedback from the humans is used to improve the pattern detection and facilitate the detection of actionable insights.

Abstract: Methods for analyzing and rendering business intelligence data allow for efficient scalability as datasets grow in size. Human intervention is minimized by augmented decision making ability in selecting what aspects of large datasets should be focused on to drive key business outcomes. Variable value combinations that are predominant drivers of key observations are automatically determined from several competing variable value combinations. The identified variable value combinations can then be then used to predict future trends underlying the business intelligence data. In another embodiment, an observed outcome is decomposed into multiple contributing drivers and the impact of each of the contributing drivers can be analyzed and numerically quantified—as a static snapshot or as a time-varying evolution.

Abstract: Operations, such as data processing operations, can be improved by applying clustering and statistical techniques to observed behaviors in the data processing operations.

Abstract: Business process providers implement client data processing processes for clients. The business process providers use certain formats (provider formats, defined by provider format fields). The clients use certain formats (client formats, defined by client format fields). A normative format is automatically defined and used as an intermediary between client and provider formats. Mappings between the normative format and the client and provider formats can be generated automatically.

Abstract: A combined computer/human approach is used to detect actionable insights in large data sets. Automated computer analysis used to identify patterns (e.g., possibly meaningful patterns or subsets within the data). These are presented to humans for feedback, where the humans may have little to no training in the statistical methods used to detect actionable insights. Feedback from the humans is used to improve the pattern detection and facilitate the detection of actionable insights.

Abstract: Business process providers implement client data processing processes for clients. The business process providers use certain formats (provider formats, defined by provider format fields). The clients use certain formats (client formats, defined by client format fields). A normative format is automatically defined and used as an intermediary between client and provider formats. Mappings between the normative format and the client and provider formats can be generated automatically.

Abstract: Business process provider(s) process client data. The clients use certain formats (client formats, defined by client format fields). The client format fields instantiated in documents are analyzed. Based on this analysis, the client processes are automatically grouped into different process platforms for processing. For example, similar client processes preferably are grouped together into the same process platform, in order to increase efficiency of processing. In another aspect, the user interfaces used by the business process provider(s) may be constructed from different blocks, where the blocks are automatically defined based on the analysis of client format fields.

Abstract: Operations, such as data processing operations, can be improved by applying clustering and statistical techniques to observed behaviors in the data processing operations.

Abstract: A method, system and computer program product for processing documents containing restricted information. One aspect concerns identifying which sections of a document may be critical, non-critical or possibly critical.

Abstract: A method, system and computer program product for processing documents containing restricted information. One aspect concerns storing documents in a distributed but secure manner, for example using keysets.

Abstract: A method, system and computer program product for processing documents containing restricted information. One aspect concerns updating the relevant information security rules applicable to the documents.

Abstract: Various embodiments of the invention provide systems and methods for estimating a frequency of errors for data processing operators, but without human inspection of documents typed by the operators and without comparing documents typed by the operators against known transcriptions.

Abstract: A method, system and computer program product for processing documents containing restricted information. The method for processing the documents includes the step of identifying data fields in the document. The method further includes the steps of identifying a critical section(s) containing data corresponding to restricted information, and shuffling this data to create synthesized documents.

Abstract: A method, system and computer program product for processing documents containing restricted information. The method for processing the documents includes the step of identifying data fields in the document. The method further includes the steps of identifying a critical section containing data fields corresponding to restricted information, and dividing the document into a critical section and a non-critical section. Further, the method includes the step of processing the non-critical section at a minimally secure location.

Abstract: Methods and systems for automated assessment of the operational risk for an organization based on sampling the operations of the organization. These approaches can be used for automated comparison of the operational risk of two or more organizational operations.

Abstract: Methods and systems for improving a data processing operation based on operator-specific quality management and/or monitoring. For example, operator-specific frequency of errors, error rates, error patterns and/or root causes may be identified. Operator-specific actions may then be taken based on these.