Google Inc.

Clearstory Data Inc.

Douglas Van Der Molen

Hui Sok Moon

Lik Mui

Jan Matthias Ruhl

Japjit Tulsi

Vaibhav Nivargi

Nicholas Briggs

Nick Rabinowitz

Mikhail Bautin

Jon Hartlaub

Kevin Malone

A server has a data processing module with instructions executed by a processor to maintain an annotation of a first visualization of data, where the first visualization of data has visualization configuration parameters. The annotation is linked to a second visualization of the data that utilizes the visualization configuration parameters.

Apparatus and method for state management across visual transitions

A server includes a data ingestion module with executable instructions executed by a processor to produce inferred data types from received data and utilize the inferred data types to augment the received data with computed values that aggregate the received data along a hierarchical dimension.

Apparatus and method for ingesting and augmenting data

A server includes a data processing module with executable instructions executed by a processor to produce a first inferred data type from first received data and a second inferred data type from second received data. The first inferred data type is utilized to augment the first received data with computed values that aggregate the first received data along a first hierarchical dimension. The second inferred data type is utilized to augment the second received data with computed values that aggregate the second received data along a second hierarchical dimension. The first hierarchical dimension and the second hierarchical dimension are harmonized to a lowest common unit value. A first visualization of the first received data is provided based upon the lowest common unit value. A second visualization of the second received data is provided based upon the lowest common unit value.

Apparatus and method for harmonizing data along inferred hierarchical dimensions

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.

Method and system for detecting anomalies in web analytics data

Method and system for using web analytics data for detecting anomalies

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.

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Elmhurst, IL, United States of America

Douglas Van Der Molen