Abstract: The present embodiments relate to proactive learning of network software problems. In an embodiment, a method includes receiving, by a detection system, webpage data from a computer system. The computer system can receive the webpage data from a plurality of client devices. The webpage data can be associated with user identifiers identifying each client device of the plurality of client devices. The detection system can then receive assistance data from a user assistance computer. The user assistance computer can receive the assistance data from a plurality of user devices. The assistance data can be associated with user identifiers identifying each user device of the plurality of user devices. The detection system can label the webpage data based on the assistance data including matching user identifiers and then determine at least a pattern based on the labeled webpage data.

Abstract: Techniques are described herein for evaluating user activities on a website for detecting and alerting anomalies with the website. For example, an alerting system may determine a set of time-windows for each of a set of attribute value(s) and use a set of performance metrics and/or web session events for the time-windows from historical web sessions to detect an anomaly in a current time-window. The alerting system may determine an evaluation range based on weighted performance ranges, and alert a client indicating a performance anomaly if a performance metric at the time of evaluation are out of the evaluation range. Attribute value(s) related to a website issue can be identified based on a correlated to anomalous web session events or to an anomalous time-window.

Abstract: Techniques are described herein for determining an impact of slow performing webpages on a website. For example, a detection system may be provided to determine timing distributions for webpages by analyzing timing measurements for different timing metrics. The detection system may determine ranges of the timing distributions for slow and fast group of users of the website. The detection system may analyze user interactions of slow and fast group of users on the website. The detection system may further determine impacts of poor performing webpages on the website's performance based on the analysis of the user interactions on the webpages. The detection system may classify the impacts the webpages of the website and display the classifications to a website provider for identifying network operation(s) contributing to poor performance of the webpages.

Abstract: Techniques are described herein for evaluating user activities on a website for detecting and alerting anomalies with the website. For example, an alerting system may determine a set of time-windows for each of a set of attribute value(s) and use a set of performance metrics and/or web session events for the time-windows from historical web sessions to detect an anomaly in a current time-window. The alerting system may determine an evaluation range based on weighted performance ranges, and alert a client indicating a performance anomaly if a performance metric at the time of evaluation are out of the evaluation range. Attribute value(s) related to a website issue can be identified based on a correlated to anomalous web session events or to an anomalous time-window.

Abstract: Techniques are described herein for determining an impact of slow performing webpages on a website. For example, a detection system may be provided to determine timing distributions for webpages by analyzing timing measurements for different timing metrics. The detection system may determine ranges of the timing distributions for slow and fast group of users of the website. The detection system may analyze user interactions of slow and fast group of users on the website. The detection system may further determine impacts of poor performing webpages on the website's performance based on the analysis of the user interactions on the webpages. The detection system may classify the impacts the webpages of the website and display the classifications to a website provider for identifying network operation(s) contributing to poor performance of the webpages.

Abstract: The present embodiments relate to proactive learning of network software problems. In an embodiment, a method includes receiving, by a detection system, webpage data from a computer system. The computer system can receive the webpage data from a plurality of client devices. The webpage data can be associated with user identifiers identifying each client device of the plurality of client devices. The detection system can then receive assistance data from a user assistance computer. The user assistance computer can receive the assistance data from a plurality of user devices. The assistance data can be associated with user identifiers identifying each user device of the plurality of user devices. The detection system can label the webpage data based on the assistance data including matching user identifiers and then determine at least a pattern based on the labeled webpage data.

Abstract: Techniques are described herein for selecting a template for efficient generation of dashboards for the template that derive insights into website performance. A client can provide search criteria, and, in response, the client can view and select a template that matches the context in which the client would like insights into the website data. The client can interact with the selected template to populate dashboards for the selected template. An identification of a metric can be detected for a placeholder field for a dashboard, and a specification of event type(s) to be used in populating the metric can be obtained. The dashboard can be loaded in memory, and user session data can be processed to detect events in the user session data. Metrics can be generated in the dashboard using the events in the user session data. The metric in the dashboard can be displayed at the client device.

Abstract: The present embodiments relate to proactive learning of network software problems. In an embodiment, a method includes receiving, by a detection system, webpage data from a computer system. The computer system can receive the webpage data from a plurality of client devices. The webpage data can be associated with user identifiers identifying each client device of the plurality of client devices. The detection system can then receive assistance data from a user assistance computer. The user assistance computer can receive the assistance data from a plurality of user devices. The assistance data can be associated with user identifiers identifying each user device of the plurality of user devices. The detection system can label the webpage data based on the assistance data including matching user identifiers and then determine at least a pattern based on the labeled webpage data.

Abstract: Techniques are described herein for identifying and masking instances of private information in tracked user interactions with a network site during a network session. A scanning module can receive set of captured user interactions (e.g., movements between portions of the network site and data provided in one or more fields on the network site) with the network site during the network session. Instances of private information can be identified and a number of nodes in a document object model (DOM) for the network site that correspond to each identified instance of private information. Information about each corresponding node can be provided to update a capture agent to mask each corresponding node in future user interactions.

Abstract: Techniques are described herein for determining an impact of slow performing webpages on a website. For example, a detection system may be provided to determine timing distributions for webpages by analyzing timing measurements for different timing metrics. The detection system may determine ranges of the timing distributions for slow and fast group of users of the website. The detection system may analyze user interactions of slow and fast group of users on the website. The detection system may further determine impacts of poor performing webpages on the website's performance based on the analysis of the user interactions on the webpages. The detection system may classify the impacts the webpages of the website and display the classifications to a website provider for identifying network operation(s) contributing to poor performance of the webpages.

Abstract: Techniques are described herein for evaluating user activities on a website for detecting and alerting anomalies with the website. For example, an alerting system may determine a set of time-windows for each of a set of attribute value(s) and use a set of performance metrics and/or web session events for the time-windows from historical web sessions to detect an anomaly in a current time-window. The alerting system may determine an evaluation range based on weighted performance ranges, and alert a client indicating a performance anomaly if a performance metric at the time of evaluation are out of the evaluation range. Attribute value(s) related to a website issue can be identified based on a correlated to anomalous web session events or to an anomalous time-window.

Abstract: Techniques are described herein for determining an impact of slow performing webpages on a website. For example, a detection system may be provided to determine timing distributions for webpages by analyzing timing measurements for different timing metrics. The detection system may determine ranges of the timing distributions for slow and fast group of users of the website. The detection system may analyze user interactions of slow and fast group of users on the website. The detection system may further determine impacts of poor performing webpages on the website's performance based on the analysis of the user interactions on the webpages. The detection system may classify the impacts the webpages of the website and display the classifications to a website provider for identifying network operation(s) contributing to poor performance of the webpages.

Abstract: Techniques are described herein for determining an impact of slow performing webpages on a website. For example, a detection system may be provided to determine timing distributions for webpages by analyzing timing measurements for different timing metrics. The detection system may determine ranges of the timing distributions for slow and fast group of users of the website. The detection system may analyze user interactions of slow and fast group of users on the website. The detection system may further determine impacts of poor performing webpages on the website's performance based on the analysis of the user interactions on the webpages. The detection system may classify the impacts the webpages of the website and display the classifications to a website provider for identifying network operation(s) contributing to poor performance of the webpages.

Abstract: The present embodiments relate to proactive learning of network software problems. In an embodiment, a method includes receiving, by a detection system, webpage data from a computer system. The computer system can receive the webpage data from a plurality of client devices. The webpage data can be associated with user identifiers identifying each client device of the plurality of client devices. The detection system can then receive assistance data from a user assistance computer. The user assistance computer can receive the assistance data from a plurality of user devices. The assistance data can be associated with user identifiers identifying each user device of the plurality of user devices. The detection system can label the webpage data based on the assistance data including matching user identifiers and then determine at least a pattern based on the labeled webpage data.