Microsoft Technology Licensing, LLC

Amazon Technologies, Inc.

Anurag Mishra

Prateek Jain

Amar Budhiraja

Ayush Choure

Mohammad Luqman

Shivangi Dhakad

Lekshmi Menon

Darshatkumar Anandji Shah

Chase Kernan

Navya Yarrabelly

Gaurush Hiranandani

Juhi Dua

Divyank Duvedi

Richard Krog

A database management system obtains key ranges associated with partitions of a database table, and determines that restoration of data associated with at least one of these partitions would take time that is estimated to exceed a goal time. The database management system splits a partition into two or more additional partitions, so that the respective estimated restoration times for data associated with the additional partitions is estimated to be less than the goal time. The database management system then causes the database table to be restored according to partitions that comprises the additional partitions.

Database table restoration for data with skewed distribution

Systems and methods for entity recommendation can make use of rich data by allowing the items to be recommended and the recipients of the recommendation (e.g., users) to be modeled as 'complex entities' composed of one or more static sub-entities and/or a dynamic component, and by utilizing information about multiple relationships between the sub-entities as reflected in bipartite graphs. Generating recommendations from such information may involve creating vector representations of the sub-entities based on the bipartite graphs (e.g., using graph-based convolutional networks), and combining these vector representations into representations of the items and users (or other recipients) to be fed into a classifier model.

Multiple-entity-based recommendation system

Message recommendation system

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Redmond, WA, United States of America

Anurag Mishra