Amazon Technologies, Inc.

Karthik Gurumoorthy Subramanya Bharathy

Omer Ahmed Zaki

Yannis Papakonstantinou

Rohan Deshpande

Rajib Dugar

Shivateja Reddy Ummenthala

Timothy Andrew Rath

Gaurav Saxena

Mustafa Ozan Ozen

Bradley R Bebee

Lonnie J Princehouse

George Karypis

Gaurav Gupta

Divyakala Vel

Xiang Song

Ankit Gupta

Adesoji Adeshina

A computer-implemented method performed at least in part by a graph database configured to store at least one graph dataset. The method includes receiving a graph query configured to be performed against a machine learning model, and communicating the graph query with a machine learning system that is configured to use the machine learning model to obtain model inference results and communicate those model inference results to the graph database application. The graph database provides query results based at least in part on the model inference results to an entity.

Using graph queries to obtain results from machine learning models

Methods, systems, and computer-readable media for continuous functions in a time-series database are disclosed. A plurality of data points of a time series are stored into one or more storage tiers of a time-series database. The plurality of data points comprise a plurality of discrete measurements at respective timestamps. Using one or more query processors of the time-series database, a query of the time series is initiated. The query indicates a time range. Using the one or more query processors, a continuous function is determined that represents a segment of the time series in the time range. The continuous function is determined based at least in part on the plurality of data points. An operation is performed using the continuous function as an input.

Continuous functions in a time-series database

Materialized views may be generated at a managed materialized view platform that accepts data streams as a source. When updating a materialized view with a data stream as a source, a stored portion of the stream is used to maintain a history of changes to be made. When a join operation needs to utilize the data stream source to determine an update to the materialized view, the data stream is preserved to complete the join and update the materialized view.

Maintaining data stream history for generating materialized views

The maintenance of a materialized view created and managed by a materialized view management platform may be dynamically adjusted. The performance of updates to a materialized view are monitored to trigger different events to make adjustments to the performance of subsequent updates. The materialized view management platform can adequately scale to handle changes to data sources as inputs to the maintenance of the materialized view, as well as make other adjustments handle various changes in the capability of the target data store to receive updates to the materialized view (e.g., target data store outages).

Dynamically adjusting performance of materialized view maintenance

Managed materialized views may be generated from across heterogeneous data sources. A request to create a materialized view may be received and performed by a materialized view management platform, which may obtain and generate the materialized view from different data sources and store the materialized view in a target system. Changes to the data sources may be obtained at the materialized view management platform and updates to the materialized view may be determined. The materialized view in the target system may be updated.

Managed materialized views created from heterogenous data sources

A materialized view created by a materialized view management platform may be replicated across many different target systems. The materialized view management platform allows users to specify different target systems. To create the materialized view in the different target systems, the materialized view management platform can translate the materialized view into different formats in order to store the materialized view in the different formats. Updates to the different instances of the materialized view are then made to keep the different materialized view formats in the different target systems up-to-date.

Replicating materialized views across heterogeneous target systems

Managed materialized views created from heterogeneous data sources

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Sammamish, WA, United States of America

Karthik Gurumoorthy Subramanya Bharathy