Abstract: An analytics engine (AE) computing system for analyzing and evaluating data in real-time associated with a performance of parties interacting within a multi-party interaction is provided. The AE system is configured to receive interaction data from a data validation (DV) computing device, retrieve contextual data from a contextual data source, determine a task identifier, and calculate a task score. The AE system is also configured to retrieve normalization model data from a normalization database, compare a plurality of normalization rules to the validated interaction data and the contextual data, and determine at least one normalization factor applies to the task score. The AE system is further configured to normalize the task score based on the at least one normalization factor, calculate an aggregate score using the normalized task score, and store the validated interaction data, the normalized task score, and the aggregate score in an analysis database.

Abstract: An analytics engine (AE) computing system for analyzing and evaluating data in real-time associated with a performance of parties interacting within a multi-party interaction is provided. The AE system is configured to receive interaction data from a data validation (DV) computing device, retrieve contextual data from a contextual data source, determine a task identifier, and calculate a task score. The AE system is also configured to retrieve normalization model data from a normalization database, compare a plurality of normalization rules to the validated interaction data and the contextual data, and determine at least one normalization factor applies to the task score. The AE system is further configured to normalize the task score based on the at least one normalization factor, calculate an aggregate score using the normalized task score, and store the validated interaction data, the normalized task score, and the aggregate score in an analysis database.

Abstract: An analytics engine (AE) computing system for analyzing and evaluating data in real-time associated with a performance of parties interacting within a multi-party interaction is provided. The AE system is configured to receive interaction data from a data validation (DV) computing device, retrieve contextual data from a contextual data source, determine a task identifier, and calculate a task score. The AE system is also configured to retrieve normalization model data from a normalization database, compare a plurality of normalization rules to the validated interaction data and the contextual data, and determine at least one normalization factor applies to the task score. The AE system is further configured to normalize the task score based on the at least one normalization factor, calculate an aggregate score using the normalized task score, and store the validated interaction data, the normalized task score, and the aggregate score in an analysis database.

Abstract: An analytics engine (AE) computing system for analyzing and evaluating data in real-time associated with a performance of parties interacting within a multi-party interaction is provided. The AE system is configured to receive interaction data from a data validation (DV) computing device, retrieve contextual data from a contextual data source, determine a task identifier, and calculate a task score. The AE system is also configured to retrieve normalization model data from a normalization database, compare a plurality of normalization rules to the validated interaction data and the contextual data, and determine at least one normalization factor applies to the task score. The AE system is further configured to normalize the task score based on the at least one normalization factor, calculate an aggregate score using the normalized task score, and store the validated interaction data, the normalized task score, and the aggregate score in an analysis database.

Abstract: An analytics engine (AE) computing system for analyzing and evaluating data in real-time associated with a performance of parties interacting within a multi-party interaction is provided. The AE system is configured to receive interaction data from a data validation (DV) computing device, retrieve contextual data from a contextual data source, determine a task identifier, and calculate a task score. The AE system is also configured to retrieve normalization model data from a normalization database, compare a plurality of normalization rules to the validated interaction data and the contextual data, and determine at least one normalization factor applies to the task score. The AE system is further configured to normalize the task score based on the at least one normalization factor, calculate an aggregate score using the normalized task score, and store the validated interaction data, the normalized task score, and the aggregate score in an analysis database.

Abstract: An analytics engine (AE) computing system for analyzing and evaluating data in real-time associated with a performance of parties interacting within a multi-party interaction is provided. The AE system is configured to receive interaction data from a data validation (DV) computing device, retrieve contextual data from a contextual data source, determine a task identifier, and calculate a task score. The AE system is also configured to retrieve normalization model data from a normalization database, compare a plurality of normalization rules to the validated interaction data and the contextual data, and determine at least one normalization factor applies to the task score. The AE system is further configured to normalize the task score based on the at least one normalization factor, calculate an aggregate score using the normalized task score, and store the validated interaction data, the normalized task score, and the aggregate score in an analysis database.

Abstract: An analytics engine (AE) computing system for analyzing and evaluating data in real-time associated with a performance of parties interacting within a multi-party interaction is provided. The AE system is configured to receive interaction data from a data validation (DV) computing device, retrieve contextual data from a contextual data source, determine a task identifier, and calculate a task score. The AE system is also configured to retrieve normalization model data from a normalization database, compare a plurality of normalization rules to the validated interaction data and the contextual data, and determine at least one normalization factor applies to the task score. The AE system is further configured to normalize the task score based on the at least one normalization factor, calculate an aggregate score using the normalized task score, and store the validated interaction data, the normalized task score, and the aggregate score in an analysis database.

Abstract: An analytics engine (AE) computing system for analyzing and evaluating data in real-time associated with a performance of parties interacting within a multi-party interaction is provided. The AE system is configured to receive interaction data from a data validation (DV) computing device, retrieve contextual data from a contextual data source, determine a task identifier, and calculate a task score. The AE system is also configured to retrieve normalization model data from a normalization database, compare a plurality of normalization rules to the validated interaction data and the contextual data, and determine at least one normalization factor applies to the task score. The AE system is further configured to normalize the task score based on the at least one normalization factor, calculate an aggregate score using the normalized task score, and store the validated interaction data, the normalized task score, and the aggregate score in an analysis database.

Abstract: An analytics engine (AE) computing system for analyzing and evaluating data in real-time associated with a performance of parties interacting within a multi-party interaction is provided. The AE system is configured to receive interaction data from a data validation (DV) computing device, retrieve contextual data from a contextual data source, determine a task identifier, and calculate a task score. The AE system is also configured to retrieve normalization model data from a normalization database, compare a plurality of normalization rules to the validated interaction data and the contextual data, and determine at least one normalization factor applies to the task score. The AE system is further configured to normalize the task score based on the at least one normalization factor, calculate an aggregate score using the normalized task score, and store the validated interaction data, the normalized task score, and the aggregate score in an analysis database.

Abstract: A wireless access architecture, having redistributed wireless access functions, comprises a plurality of cell sites connected to a main switching point. The cell sites communicate with cellular users within their respective cell site areas and comprise a communication segment for exchanging communication signals with the cellular users. The main switching point controls the communication with the cellular users and the communication segment and includes a processing segment for processing the communication signals from the cell sites. The processing segment includes a segment for modulating and demodulating the communication signals, and a segment for bit stream processing the communication signals.