International Business Machines Corporation

Acoustic, L.p.

Travis Spence Powell

Robert I Wenig

Nadav Caspi

Ashwin Singhania

Wolf Herda

Gerard Dietrich

Mikhail Borisovich Kozine

A privacy processing system may use privacy rules to filter sensitive personal information from web session data. The privacy processing system may generate privacy profiles or privacy metadata that identifies how often the privacy rules are called, how often the privacy rules successfully complete actions, and the processing time required to execute the privacy rules. The privacy profiles may be used to detect irregularities in the privacy filtering process that may be associated with a variety of privacy filtering and web session problems.

Method and apparatus for generating privacy profiles

A monitoring system intelligently captures Document Object Model (DOM) events. The DOM events may provide state information that may not usually be captured during a web session. To reduce processing bandwidth, content identifiers may be used to represent some DOM events. Checkpoints may be identified during the web session and a current state of the webpage may be captured to provide replay synchronization. Different data may be captured based on a sequence and timing of the DOM events during the original web session. Data exchanged with third party websites also may be selectively captured to provide a more through simulation of the original web session.

Method and apparatus for intelligent capture of document object model events

Web session events are captured during different web sessions. A segmentation model is generated based on a number of occurrences of the web session events for different time stamp periods. The segmentation model plots a segmentation graph that may identify differences between the web session events with respect to time factors. The segmentation model may use the whole dataset of event occurrences as an input without any preliminary data segmentation or discrimination. The model can associate the web session events with the different geographic locations, reveal possible reasons for customer experience difference for the users from different locations and provide statistically sound explanation of this difference. The model is scalable and may work with big data acquired by web-based commerce sites with wide international customer base.

Method and apparatus for customer experience segmentation based on a web session event variation

A two-module system is created for automated web activity monitoring. A model is generated and model outliers are identified by the first module of the system. Reports are generated that identify the events based on their significance to the outliers. The model may be automatically and periodically regenerated for different historical time periods of the web sessions. New groups of events may be periodically extracted from new web sessions and applied to the previously generated model by the second module of the system. Model outliers may be identified from the new groups of events. The new events may be analyzed and reported to a web session operator based on their contribution to any identified outliers. Even if no outliers are detected, the new events having a most significant impact on web session operating conditions may be identified and reported in real-time.

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San Francisco, CA, United States of America

Travis Spence Powell