In general, large-scale data processing systems process large amounts of data from various sources and/or machines. As a specific example, large-scale machine learning systems may process large amounts of training data from data streams received by the system. A data stream may include training examples corresponding to specific instances of an event or action such as when a user selects a specific search result, or when a single video is viewed from among multiple videos presented to a user. An example may contain features (i.e., observed properties such as a user being located in the USA, a user preferring to speak English, etc.) and may also contain a label which may indicate an event or action associated with the example (e.g., a user selected a specific search result, a user did not select a specific search result, a user viewed a particular video, etc.). These training examples may be used to generate statistics for each of the features. As new examples enter the system, a statistic associated with a feature may need to be updated. However, storing and updating these statistics can require infeasible amounts of storage and can reduce the processing speed of such systems.
USPTO Backgrounds
USPTO Backgrounds