Amazon Technologies, Inc.

Vamshi Krishna Surabhi

Dinesh Thangaraju

Kirtan Modi

Robert James Victor

Xinyu Zhou

Qie Hu Huang

Abhishek Verma

Shuang Li

Vykunth Ashok

Guanxu Yu

Rajat Arora

Homayoun Taherian

John Thomas Winters

Xuefeng Zhai

Yongzhen Lu

Ying Zhang

Alok Tiwari

Bonney Varghese

Nitin Kishore Gupta

Features are disclosed for managing multiple heterogeneously owned data stores (e.g., data sets, data lakes) and provisioning a framework for data consumers and data publishers. A computing device can obtain a plurality of data catalogs associated with the data stores. For example, the computing device may update a hybrid data catalog with information from the plurality of data catalogs. The computing device can further provide a portion of the plurality of data catalogs to a data consumer. The computing device may provide the portion of the plurality of data catalogs based on permissions provided by the data publisher. In response, the computing device can receive a request to access a data store associated with the plurality of data catalogs. The computing device can transmit the request to a corresponding data publisher and, based on a response by the data publisher, may modify the distinct access controls for the data store.

Managing data permissions for disparate data sets

A permissions management service may allow a large number of user to access database objects of a database service in a scalable manner. After a data owner on-boards a database of a database service with the permissions management service, the data owner may create a data catalog for a user or user group that indicates the database objects (e.g., tables, views) that are available for the user to request access to. A request from a user may be authenticated by the permissions management service using federation/single sign-on. The user may select database objects from a data catalog of objects that are available for the user to request access to. The permissions management service sends an access request to the database service, indicating the selected database objects. The database service may then grant to the user permission to access the selected objects (e.g., via grant commands).

Scalable permissions management for granular levels of database access

Devices, systems, and methods are provided for on-target rate optimization for video. A method may include receiving streaming video advertisement impression data; receiving user activity data indicative of day-parts when viewers watch content; generating, based on the streaming video advertisement impression data and the survey data, using a machine learning model, a demographic probability vector, wherein each entry of the demographic probability vector is indicative of a probability that a viewer is in a respective age range of the non-overlapping demographic groups; generating, using the machine learning model, an audience recognition model with the demographic probability vector; generating a synthetic audience model predicting future advertisement viewing behavior; generating an assignment of an advertisement bid to a respective demographic group of the non-overlapping demographic groups; and generating, based on the assignment, a list of target demographic groups of the non-overlapping demographic groups for a bid request associated with the advertisement bid.

Enhanced on-target rate optimization for video using machine learning

Techniques for routing items addressed to an unstructured address are described. One embodiment includes receiving an order for delivery of a first package, the order specifying a first address that does not comply with a defined address format. The first address is processed using one or more hybrid machine learning algorithms to determine a Normalized Delivery Location (NDL) associated with the first address. A sorting zone that encompasses the NDL is determined. The sorting zones correspond to a predefined geographic region. Embodiments facilitate transport of the first package to a physical shipping location within the predefined geographic region.

Normalizing addresses to facilitate sortation and routing solution using natural language text processing

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Redmond, WA, United States of America

Vamshi Krishna Surabhi