Abstract: In one example, a data package created by a particular wireless mobile device, and comprising metrics collected by the wireless mobile device, is retrieved. The metrics concern performance of the wireless mobile device in a wireless communication network, and are transformed into measures that enable derivation of a key performance indicator concerning the performance of the wireless mobile device. An enriched measure is generated by processing the measures based on correlations of wireless mobile device events in a time sequence, and the enriched measure is transformed into a KPI that is specific to the performance of the wireless mobile device. The KPI of the wireless mobile device is compared to a KPI of the population of wireless mobile devices, and results of the comparison of KPIs serve as a basis to resolve a problem regarding operation of the wireless mobile device.

Abstract: In one example, a data package created by a particular wireless mobile device, and comprising metrics collected by the wireless mobile device, is retrieved. The metrics concern performance of the wireless mobile device in a wireless communication network, and are transformed into measures that enable derivation of a key performance indicator concerning the performance of the wireless mobile device. An enriched measure is generated by processing the measures based on correlations of wireless mobile device events in a time sequence, and the enriched measure is transformed into a KPI that is specific to the performance of the wireless mobile device. The KPI of the wireless mobile device is compared to a KPI of the population of wireless mobile devices, and results of the comparison of KPIs serve as a basis to resolve a problem regarding operation of the wireless mobile device.

Abstract: In one example, a data package created by a particular wireless mobile device, and comprising metrics collected by the wireless mobile device, is retrieved. The metrics concern performance of the wireless mobile device in a wireless communication network, and are transformed into measures that enable derivation of a key performance indicator concerning the performance of the wireless mobile device. An enriched measure is generated by processing the measures based on correlations of wireless mobile device events in a time sequence, and the enriched measure is transformed into a KPI that is specific to the performance of the wireless mobile device. The KPI of the wireless mobile device is compared to a KPI of the population of wireless mobile devices, and results of the comparison of KPIs serve as a basis to resolve a problem regarding operation of the wireless mobile device.

Abstract: A method for a plurality of processors configured to perform steps in a map/reduce network operation adds incremental batch transformation of sequential measures recorded by time periods and uploaded asynchronously from their capture on mobile devices. The method creates and tracks measure states for each measure. The current recurrence of a measure is a transformation of selected past recurrences and measures. Measure state is propagated according to rules. The ID for a current measure is derived from the IDs of its cache measures and the IDs of its trigger measures. A batch incremental enrichment transforms one or more measures from one or more recurrences into at least one output measure that may be transformed again by the same or another batch incremental enrichment. The apparatus determines if the value needs to be overwritten with a newer value by the type of transformation, the recurrence id and the state.

Abstract: A data services handler comprises an interface between a data store and applications that supply and consume data, and a real time information director (RTID) that transforms data under direction of polymorphic metadata that defines a security model and data integrity rules for application to the data.

Abstract: A template for deployment of enterprise applications one example of which is an interaction manager. For deployment of the interaction manager, the template is configured with a class framework for building source code of the interaction manager and a test driver. The template is further configured with wizards. An application wizard provides for building a base class portion of the class framework. Interaction and attribute wizards provide for extending or modifying the class framework once the base class portion is built so as to provide for enterprise-specific deployment of the interaction manager along with the test driver. In addition, test driver script is provided through which the test driver is to be used for validating and measuring the performance of the interaction manager once their source code is converted to executable code.

Abstract: Knowledge discovery through analytic learning cycles is founded on a coherent, real-time view of data from across an enterprise, the data having been captured and aggregated and is available in real-time at a central repository. Knowledge discovery is an iterative process where each cycle of analytic learning employs data mining. Thus, an analytic learning cycle includes defining a problem, exploring the data at the central repository in relation to the problem, preparing a modeling data set from the explored data, building a model from the modeling data set, assessing the model, deploying the model back to the central repository, and applying the model to a set of inputs associated with the problem. Application of the model produces results and, in turn, creates historic data that is saved at the central repository.

Abstract: An interaction manager (IM) is provided which is designed for gathering information associated with customer interactions, loading customer-related data at the beginning of each session, enriching the customer interactions with offers from a rules service, and performing data caching for more efficient customer interaction data retrieval and processing, including caching a session context with the gathered information and customer-related data after each interaction and restoring the session context at the beginning of each interaction. Offers to the customers are based on a comprehensive real-time view of the customer-related data and are augmented by rules and/or data mining, wherein the rules include enterprise rules and/or policies. The data caching includes saving data in denormalized form.