Abstract: Systems and methods for defining a custom segment in a set of behavioral data are provided. A described method includes receiving a set of behavioral data associated with a plurality of user devices and identifying multiple cohort groups, each of the cohort groups including one or more of the user devices. The behavioral data includes a behavior metric for each of the user devices and the cohort groups are identified based on the behavior metric for each of the user devices. The method further comprises generating a segmentation interface including a graphical visualization of the multiple cohort groups and causing the segmentation interface to be presented via a user interface device. The method further comprises defining a custom segment of the behavioral data based on a user selection of one or more of the multiple cohort groups via the segmentation interface.

Abstract: A system and method for analyzing traffic to a website is provided that is based on log files and that uses both server-side and client-side information channeled through one source to create a more complete picture of activity to a website. In one preferred embodiment, a sensor code is embedded in a requested web page, and sends information back to the web server where the website resides. This additional information is logged along with normal requests.

Abstract: Systems and methods for defining a custom segment in a set of behavioral data are provided. A described method includes receiving a set of behavioral data associated with a plurality of user devices and identifying multiple cohort groups, each of the cohort groups including one or more of the user devices. The behavioral data includes a behavior metric for each of the user devices and the cohort groups are identified based on the behavior metric for each of the user devices. The method further comprises generating a segmentation interface including a graphical visualization of the multiple cohort groups and causing the segmentation interface to be presented via a user interface device. The method further comprises defining a custom segment of the behavioral data based on a user selection of one or more of the multiple cohort groups via the segmentation interface.

Abstract: Systems, methods and apparatus for analyzing Internet traffic. In an aspect, a method receives at a server from a client device a report request for a report related to web site traffic; in response to the report request, sends from the server web site traffic data and application code to the client device. The application code has instructions that cause the client device to: generate a report to display the web site traffic data, time the display of the web site traffic data, periodically request updated web site traffic data according to the time of the display, and update the report with the updated web site traffic data; and the method sends from the server to the client device the updated web site traffic data in response to the request for updated web site traffic data.

Abstract: A publisher web page is rendered at a client device from a publisher and the client device issues a request for traffic statistics data related to traffic content in the web page. The client device receives the traffic statistics data and overlays the traffic statistics data on the web page, e.g., proximate to the related traffic content in the web page.

Abstract: A server system stores web analytics data for a web page in a device. The web analytics data comprises a plurality of prior time-value pairs, each pair including a value of an attribute associated with the web page and a time associated with the value. For a particular attribute, the server system collects a new time-value pair including a new value associated with the web page and a new time indicating when the value was determined. The server system estimates a predicted value for the attribute and an associated error-variance at the new time by applying a forecasting model to the prior time-value pairs in respective subsets of the web analytics data. The collected new time-value pair is tagged if its value is outside the error variance of the predicted value for the particular attribute.

Abstract: A computer-implemented method for visualizing a multi-dimensional dataset at a client device is disclosed. The client device displays a first view of a subset of the multi-dimensional dataset, including displaying dimension data of a first reference dimension attribute and metric data of a first metric attribute that corresponds to the respective first reference dimension data along a first axis. After receiving a user request to partition the metric data of the first metric attribute by a first pivot dimension attribute, the client device requests and receives dimension data of the first pivot dimension attribute and the corresponding partitioned metric data of the first metric attribute from a server system and displays a second view of the subset of the multi-dimensional dataset, including displaying the first pivot dimension data and the corresponding partitioned metric data of the first metric attribute along the second axis.

Abstract: A system and method for analyzing traffic to a website is provided that is based on log files and that uses both server-side and client-side information channeled through one source to create a more complete picture of activity to a website. In one preferred embodiment, a sensor code is embedded in a requested web page, and sends information back to the web server where the website resides. This additional information is logged along with normal requests.

Abstract: A server system stores time series data for a data source. The time series data comprises a plurality of time-value pairs, each pair including a value associated with an attribute of the data source and a time. For a particular attribute, the server system generates a plurality of forecasting models for characterizing the time-value pairs, each model including an estimated attribute value and an associated error-variance. For a time-value pair, the server system determines a plurality of differences between the value of the time-value pair and respective estimated attribute values of the plurality of forecasting models and tags the time-value pair as an anomaly if the differences for at least a first subset of the forecasting models are greater than the corresponding error variances. In response to a request from a client application, the server system returns at least a subset of the time-value pairs tagged as anomalies.

Abstract: A system and method for analyzing traffic to a website is provided that is based on log files and that uses both server-side and client-side information channeled through one source to create a more complete picture of activity to a website. In one preferred embodiment, a sensor code is embedded in a requested web page, and sends information back to the web server where the website resides. This additional information is logged along with normal requests.

Abstract: A system and method for analyzing traffic to a website is provided that is based on log files and that uses both server-side and client-side information channeled through one source to create a more complete picture of activity to a website. In one preferred embodiment, a sensor code is embedded in a requested web page, and sends information back to the web server where the website resides. This additional information is logged along with normal requests.

Abstract: A computer-implemented method for displaying annotations for a multidimensional dataset at a client device is disclosed. The client device is connected to a server system through a network. The client device displays a first view of the multidimensional dataset. The first view includes a first graph over a period of time and a plurality of annotations, each annotation corresponding to a respective time segment within the period of time. Upon receiving a user selection of one of the plurality of annotations, the client device displays a second view of the multidimensional dataset. The second view includes a second graph over the period of time and the plurality of annotation. Finally, the client device highlights the user-selected annotation among the plurality of annotations and the time segment corresponding to the user-selected annotation in the second graph.

Abstract: A server system stores web analytics data for a web page in a device. The web analytics data comprises a plurality of prior time-value pairs, each pair including a value of an attribute associated with the web page and a time associated with the value. For a particular attribute, the server system collects a new time-value pair including a new value associated with the web page and a new time indicating when the value was determined. The server system estimates a predicted value for the attribute and an associated error-variance at the new time by applying a forecasting model to the prior time-value pairs in respective subsets of the web analytics data. The collected new time-value pair is tagged if its value is outside the error variance of the predicted value for the particular attribute.

Abstract: Systems, methods and apparatus for analyzing Internet traffic. In an aspect, a method receives at a server from a client device a report request for a report related to web site traffic; in response to the report request, sends from the server web site traffic data and application code to the client device. The application code has instructions that cause the client device to: generate a report to display the web site traffic data, time the display of the web site traffic data, periodically request updated web site traffic data according to the time of the display, and update the report with the updated web site traffic data; and the method sends from the server to the client device the updated web site traffic data in response to the request for updated web site traffic data.

Abstract: A server system stores time series data for a data source. The time series data comprises a plurality of time-value pairs, each pair including a value associated with an attribute of the data source and a time. For a particular attribute, the server system generates a plurality of forecasting models for characterizing the time-value pairs, each model including an estimated attribute value and an associated error-variance. For a time-value pair, the server system determines a plurality of differences between the value of the time-value pair and respective estimated attribute values of the plurality of forecasting models and tags the time-value pair as an anomaly if the differences for at least a first subset of the forecasting models are greater than the corresponding error variances. In response to a request from a client application, the server system returns at least a subset of the time-value pairs tagged as anomalies.

Abstract: A publisher web page is rendered at a client device from a publisher and the client device issues a request for traffic statistics data related to traffic content in the web page. The client device receives the traffic statistics data and overlays the traffic statistics data on the web page, e.g., proximate to the related traffic content in the web page.

Abstract: A server system stores web analytics data for a web page in a device. The web analytics data comprises a plurality of prior time-value pairs, each pair including a value of an attribute associated with the web page and a time associated with the value. For a particular attribute, the server system collects a new time-value pair including a new value associated with the web page and a new time indicating when the value was determined. The server system estimates a predicted value for the attribute and an associated error-variance at the new time by applying a forecasting model to the prior time-value pairs in respective subsets of the web analytics data. The collected new time-value pair is tagged if its value is outside the error variance of the predicted value for the particular attribute.

Abstract: A server system stores time series data for a data source. The time series data comprises a plurality of time-value pairs, each pair including a value associated with an attribute of the data source and a time. For a particular attribute, the server system generates a plurality of forecasting models for characterizing the time-value pairs, each model including an estimated attribute value and an associated error-variance. For a time-value pair, the server system determines a plurality of differences between the value of the time-value pair and respective estimated attribute values of the plurality of forecasting models and tags the time-value pair as an anomaly if the differences for at least a first subset of the forecasting models are greater than the corresponding error variances. In response to a request from a client application, the server system returns at least a subset of the time-value pairs tagged as anomalies.

Abstract: Systems, methods and apparatus for analyzing Internet traffic. In an aspect, a method receives at a server from a client device a report request for a report related to web site traffic; in response to the report request, sends from the server web site traffic data and application code to the client device, the application code comprising instructions that cause the client device to: generate a report to display the web site traffic data, time the display of the web site traffic data, periodically request updated web site traffic data according to the time of the display, and update the report with the updated web site traffic data; and the method sends from the server to the client device the updated web site traffic data in response to the request for updated web site traffic data.

Abstract: A computer-implemented method for generating and sharing dataset segment schemes of a multidimensional dataset at a client device is disclosed. The client device displays a first filter definition template and an attribute list. Upon detecting a user selection of a first attribute, the client device displays the first attribute and one or more candidate filter conditions in the first filter definition template. Upon detecting a user selection of a first filter condition in the first filter definition template, the client device submits a segment scheme generation request to the server system and receives from the server system a first segment identifier; and displays a first segment link at the client device, the first segment link including the first segment identifier.