Amazon Technologies, Inc.

Robert Eicher Simmering

Harsha Ramalingam

Leon Robert Warman

Thomas Albert Faulhaber, Jr

Bhavnish H Lathia

Harshal Dilip Wanjari

Erik Warren Selberg

Sanjeev Kumar

Hsuan-Cheng Lai

Ayman Mahfouz

Pratik GuhaRay

Ramasubramanian Balagurunathan

Juan Martin Borgnino

Mark Austin Buckley

Advertisements for brands that are to be aired during media programs are selected based on similarities between the brands and creators of the media programs. As a media program is being aired, data representing content of a media program is processed to identify words being spoken or sung during the media program and sentiments associated with the media program. The media program is classified based on the words and sentiments, and a classification of the media program is compared to attributes of advertisements to determine which of the advertisements is best suited for airing during the media program. Additionally, a set of words that, if spoken or sung during a media program would establish conditions favorable to a given advertisement may be identified and provided to a creator.

Selecting advertisements for media programs and establishing favorable conditions for advertisements

Selecting advertisements for media programs by matching brands to creators

Techniques are described for scheduling data access jobs based on a job dependency analysis. Preliminary data access jobs subsequently accessed by a primary data access job are identified. Execution durations of the primary data access job and the preliminary data access jobs are determined based on historical data. A total execution duration of subsets of serially dependent preliminary data access jobs and predicted execution duration of primary data access job are determined. A time-sensitive subset is identified and start times for the data access job are scheduled. The results of the preliminary data access jobs are provided to the primary data access job prior to the start time of the primary data access job so the primary data access job completes by a target completion duration.

Scheduling data access jobs based on job priority and predicted execution time using historical execution data

Techniques are described for scheduling data access jobs based on a job dependency analysis. A requested primary data access job is analyzed to determine one or more preliminary data access jobs on which it depends, and an execution duration of each data access job is predicted based on historical data or other factors. A time-sensitive subset of the preliminary data access jobs is determined as the subset of those serially dependent preliminary data access jobs for which there is a minimum time difference between the total predicted execution duration and a requested target completion time. Data access jobs are scheduled with priority given to those preliminary data access jobs in the time-sensitive subset, to enable the primary data access jobs to be completed by the requested target completion times.

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Bellevue, WA, United States of America

Robert Eicher Simmering