Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method includes providing, by an online shopping platform, an online store for purchasing gift cards, receiving a selection of a gift card of a first value by a consumer, presenting a term sheet and an install payment schedule to the consumer, in response to receiving an approval by the consumer with respect to the term sheet and the install payment schedule, providing a form to the consumer to receive data relating to a credit card of the consumer, in response to receiving the data, transmit a pre-authorization request to a credit card processor, wherein the pre-authorization request asks for an authorized amount larger than the first value, in response to receiving an approval of the pre-authorization request, charging a first install payment to the credit card and placing an amount equal to the remaining install payments on hold.

Abstract: 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.

Abstract: 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.