Abstract: Provided is a system and method for detecting parity among a group of users and recommending changes to address the parity. In one example, the method may include generating parity values for a group of users, where each parity value comprises an indicator of inequity for a value of a respective user with respect to corresponding values of other users in the group, predicting at least one category of data that most greatly influences the parity values for the group of users based on one or more machine learning models, identifying a user that has a parity value below a predetermined threshold, and determining an action which will improve the parity value of the identified user based on the at least one predicted influential category, and outputting a recommendation which includes the action.

Abstract: Under one aspect, an automated data configuration engine receives first and second sets of files that are from respective companies, include unique employee identifiers for employees respectively employed on first and second dates, and can have different formats than one another. The automated data configuration engine parses each file of the first and second sets of files to extract portions of those files corresponding to the unique employee identifiers, and generates first and second sets of database entries for each of the companies including the extracted portions and the respective first or second dates. The automated data configuration engine also obtains employee termination data for each of the respective companies; and generates a third set of database entries for each of the companies including the employee termination data of the respective company.

Abstract: A pattern analysis system enables a user to obtain specific analysis from multi-dimensional storage structures. The patterns analysis system guides the user interaction through the selection of specific available patterns. This enables the user to gain access to analytics data available in the multi-dimensional storage structure without specific knowledge of a command system syntax or semantics. Also, the user does not need knowledge of the contents or organization of the multi-dimensional storage structure.

Abstract: Techniques for graphically managing interactive analytic data include presenting a virtual workspace to a user that includes a visual representation of a simulation analysis that includes a plurality of graphical analytic data modules, each graphical analytic data module including a plurality of variables that define a business process; identifying a change to a particular variable of a particular graphical analytic data module, the change defined by a user interface (UI) adjustment of a dimension of the particular graphical analytic data module that represents the particular variable; based on the identified change to the particular variable of the particular graphical analytic data module, adjusting at least one business process output of the simulation analysis; and presenting a revised virtual workspace to the user, the revised virtual workspace including the particular graphical analytic data module and at least one business process output of the simulation analysis.

Abstract: Techniques for graphically managing interactive analytic data include presenting a virtual workspace to a user that includes a visual representation of a simulation analysis that includes a plurality of graphical analytic data modules, each graphical analytic data module including a plurality of variables that define a business process; identifying a change to a particular variable of a particular graphical analytic data module, the change defined by a user interface (UI) adjustment of a dimension of the particular graphical analytic data module that represents the particular variable; based on the identified change to the particular variable of the particular graphical analytic data module, adjusting at least one business process output of the simulation analysis; and presenting a revised virtual workspace to the user, the revised virtual workspace including the particular graphical analytic data module and at least one business process output of the simulation analysis.

Abstract: Enterprise data can be accessed via a natural language user interface. In one embodiment, a mobile application can receive voice data and text data corresponding to the voice data. A conversion from voice to text can be performed by the mobile application or a third-party dictation service. Based on the text data, a command can be generated for use by a business analytics engine or by an enterprise search engine. In the case of the business analytics engine, it can perform analysis on the retrieved enterprise data, such as by applying business algorithms on the retrieved enterprise data in order to generate analytical results. In the case of the enterprise search engine, it can perform a search of enterprise data based on the command. In either case, results can be presented to the user on a user interface.

Abstract: A pattern analysis system enables a user to obtain specific analysis from multi-dimensional storage structures. The patterns analysis system guides the user interaction through the selection of specific available patterns. This enables the user to gain access to analytics data available in the multi-dimensional storage structure without specific knowledge of a command system syntax or semantics. Also, the user does not need knowledge of the contents or organization of the multi-dimensional storage structure.

Abstract: A method and system to conduct multi-dimensional data analysis. A parameter value is applied to a first layer of a multi-dimensional data set. A simulation is conducted to evaluate the effect of the parameter value at the first layer. The effect at the first layer is automatically propagated to a second layer where a further simulation permits visualization of the effect at a different level of granularity as well as the effect of orthogonal data.

Abstract: A system for providing distributed control of a resource with centralized configuration management. A network of distributed workstations is provided for controlling a resource such as a large mainframe computer. Server workstations for applying control commands to the resource are attached to each resource, preferably through redundant connections. Remote client consoles are defined which may be connected to the server for control of that resource. Server and resource location and primary and fallback connection paths are maintained by a centralized control server. Upon client console request, the central control server causes the server workstation associated with a particular resource to establish a control session between the server and the client console. A record of each dynamic connection is maintained by the central control server. Upon failure of a client console, the server accesses central server fallback information and establishes a fallback connection with an operational client console.

Abstract: A system for providing distributed control of a resource with centralized configuration management. A network of distributed workstations is provided for controlling a resource such as a large mainframe computer. Server workstations for applying control commands to the resource are attached to each resource, preferably through redundant connections. Remote client consoles are defined which may be connected to the server for control of that resource. Server and resource location and primary and fallback connection paths are maintained by a centralized control server. Upon client console request, the central control server causes the server workstation associated with a particular resource to establish a control session between the server and the client console. A record of each dynamic connection is maintained by the central control server. Upon failure of a client console, the server accesses central server fallback information and establishes a fallback connection with an operational client console.