Ca, Inc.

Ca Technologies, Inc.

Corey Cohen

Erhan Giral

Andreas Gerhard Reiss

Cui Lin

Mark Jacob Addleman

James Andrew Perkins

Michael Steven Walker

Nicholas D Thayer

David Brooke Martin

Zaneta Svozilova

Large amounts of data from user interactions with web resources is available as data logs. Analysis may be performed to process the data log in order to determine the characteristics of the user interactions. Data log analysis may include identifying page states, which may be sets of frequent attributes and values that occur together in a session. The data log analysis may also include generating semantic labels of page states, which may describe the function of pages corresponding to different page states. Text mining models may be used to determine the semantic labels. Analysis may also include aggregating sets of page paths to create page journeys. These page journeys may be aggregated over all users, all user sessions, or other subsets of the clickstream. In some embodiments, comparing page journeys may provide recommendations for potential methods to improve the site and enhance user experiences.

Page journey determination from web event journals

Page journey determination from fingerprint information in web event journals

A system can be designed that shares transaction data across different organizations while preserving data privacy and anonymizing the source organization. Organizations can enter into an agreement to share transaction data with each other through a trusted data sharing system. The organizations can input user records that include primary and secondary explicit user identifiers into the data sharing system. The data sharing system can correlate explicit user identifiers of a user across organizations via an internal identifier without exposing explicit user identifiers. The data sharing system comprises a data anonymizer agent for each sharing organization. A data anonymizer agent selectively applies anonymization filters to quasi-identifiers in the transaction data based on quasi-identifier type to extend data privacy protection measures to quasi-identifiers without drastically reducing or eradicating the utility of the quasi-identifiers.

Privacy preserving cross-organizational data sharing with anonymization filters

For each event detected during execution of a monitored application comprising a plurality of application components, a determination is made of which of the plurality of application components corresponds to the detected event. Also, a dependency subgroup that includes the application component that corresponds to the detected event is identified, wherein a dependency subgroup indicates dependencies among a subgroup of the plurality of application components. A location within the dependency subgroup of the application component corresponding to the detected event is determined. An order of correction for the application components determined to correspond to detected events are determined based, at least in part, on the determined location. Correction of the application components determined to correspond to detected events are initiated according to the determined order of correction.

Ordered correction of application based on dependency topology

Tracking health of component in a computer system is disclosed. A health score for software components is determined for each of a plurality of time periods. The computing system determines a problem software component whose health score indicates the unhealthy status at a certain point in time. The computing system determines a set of software components that are linked by dependency relationships to the problem software component. The computing system tracks events at which software components in the set have a health score that went from the healthy status to the unhealthy status. The computing system rolls back in time through the events to locate a software component in the set that was first in time to have its health score go from the healthy status to the unhealthy status.

Tracking health status in software components

Client to server service request and response interactions are monitored. The service requests have embedded therein mobility-dedicated and business organization oriented reporting headers. At the server side of the interactions, a snooping agent picks up information from the headers, combines it with service performance metrics (e.g., time to service the request) and stores the data in a relational database. Queries are afterwards submitted to the database to spot trends including those showing emerging problems and areas for improvement in providing customers who use mobile devices with seamless and high quality service.

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Matawan, NJ, United States of America

Corey Cohen