Amdocs Development Limited

Amdocs Software Systems Limited

Liad Levi-Raz

Pavel Yefim May

Dov Rosner

Vivi Miranda

Marina Ankri

Boris Rabinovich

Idan Raphael

Moti Pinhas

Donal Burke

Yue Liu

As described herein, a system, method, and computer program provide explainability of entity data segmentation based on Boolean friction points. A dataset is processed, using a machine learning model, to calculate a plurality of Shapley values for the dataset, wherein the dataset includes friction points and explanatory variables. The dataset is clustered to generate a plurality of segments, based on the Shapley values. For each segment of the plurality of segments, a global explanation is generated for the segment using a predefined list of Boolean friction columns and the Shapley values.

System, method, and computer program for explainability of entity data segmentation based on boolean friction points

A system, method, and computer program product are provided for monitoring application activity for a cluster of applications. In use, at least one application associated with a plurality of applications to monitor is identified. Further, metadata associated with an application flow corresponding to the at least one application is defined. The defined metadata is stored in at least one file that is accessible to at least one application server associated with the plurality of applications. Activity of the at least one application is monitored utilizing the metadata associated with the at least one application. Moreover, information associated with the at least one application is displayed based on monitoring the activity of the at least one application, utilizing at least one user interface.

System, method, and computer program for monitoring application activity for a cluster of applications

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Kfar Neter, Israel

Liad Levi-Raz