Abstract: Systems, methods, and apparatuses for implementing consumer data validation, matching, and merging across tenants with optional verification prompts utilizing blockchain technologies in conjunction with a cloud based computing environment are described herein.

Abstract: Systems, methods, and apparatuses for implementing consumer data validation, matching, and merging across tenants with optional verification prompts utilizing blockchain technologies in conjunction with a cloud based computing environment are described herein.

Abstract: Systems and methods for generating a flow visualization of network traffic. A scoring function is used to prioritize which traffic paths are displayed in the visualization.

Abstract: Systems and methods for generating visualization data representing network traffic flow in or out of one or more nodes, each node representing the network traffic for one or more web pages. The method includes visualizing data configured to connect the one or more nodes with one or more connectors, each connector configured to connect two nodes, the one or more connectors configured to have a dimension that indicates a number of visits between two nodes. The network traffic capable of being highlighted when a user selects the one or more nodes or connectors.

Abstract: A method for analyzing web visitors using anonymized behavior production models begins with a website visitor accessing a website. The website includes a web beacon, which redirects the website visitor's browser to the server of a marketing services provider (MSP). The MSP server reads a cookie previously set by the MSP that includes an anonymous link. The anonymous link may be used to match data related to the website visitor that is maintained by the MSP, and browsing activity for that visitor is stored in an activity database from which periodic reports may be constructed for the website owner. The anonymous link is not associated with any personally identifiable information (PII), and thus privacy for the visitor is maintained.

Abstract: A method and system for determining a next best offer utilizes a data layer, two consumer data hubs, and a decision engine. The data layer includes numerous sources of consumer data, such as transaction data, past campaign response data, demographic data, predictive or propensity data, and real-time data such as website clickstreams. Separate consumer data hubs are used for data records that include personally identifiable information (PII) and those that do not. By using separate data hubs in this manner, online anonymous data may be used for targeting marketing, but this data may be maintained separately from PII data in order to ensure that the privacy of the consumer is protected.

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: System and method for visualizing network traffic data. Webpage visits are grouped into nodes connected by traffic paths. Steps in an online process are used to assign nodes to levels in the visualization.

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 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: Systems and methods for generating a navigation flow visualization of network traffic. The system and method configured to receive user input that identifies a node that is a grouping of network resources. Generating a visualization data of network traffic flowing into the identified node from the incoming nodes and displaying the network traffic flowing out of the identified node to the outgoing nodes. The method includes connecting the incoming nodes with the identified node using the connectors, the connectors having a width dimension that indicates a number of visits from the incoming nodes to the identified node. The method includes connecting the identified node with the outgoing nodes using the connectors that has a width dimension that indicates a number of visits from the identified node to the outgoing nodes.

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: 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: 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.

Abstract: Methods, systems and apparatus, including computer program products are described for formatting and sending data corresponding to web page events and user interactions with content displayed by on a web page to a web analytics system for storage and reporting.

Abstract: Systems and methods for generating a flow visualization of network traffic. A scoring function is used to prioritize which traffic paths are displayed in the visualization.

Abstract: A computer-implemented method for generating and sharing dataset segment schemes of a multidimensional dataset at a client device is provided. 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.