Carlo A Curino

20210124739, Apr 29, 2021

Aug 11, 2020

16/990506

- Redmond WA, US
Matteo INTERLANDI - Seattle WA, US
Fotios PSALLIDAS - Bellevue WA, US
Rathijit SEN - Madison WI, US
Kwanghyun PARK - Hudson OH, US
Ivan POPIVANOV - Redmond WA, US
Subramaniam VENKATRAMAN KRISHNAN - Santa Clara CA, US
Markus WEIMER - Kirkland WA, US
Yuan YU - Cupertino CA, US
Raghunath RAMAKRISHNAN - Bellevue WA, US
Carlo Aldo CURINO - Woodinville WA, US
Doris Suiyi XIN - Berkeley CA, US
Karla Jean SAUR - Seattle WA, US

Microsoft Technology Licensing, LLC - Redmond WA

G06F 16/2458
G06N 5/04
G06N 20/00
G06F 16/28

The description relates to executing an inference query relative to a database management system, such as a relational database management system. In one example a trained machine learning model can be stored within the database management system. An inference query can be received that applies the trained machine learning model on data local to the database management system. Analysis can be performed on the inference query and the trained machine learning model to generate a unified intermediate representation of the inference query and the trained model. Cross optimization can be performed on the unified intermediate representation. Based upon the cross-optimization, a first portion of the unified intermediate representation to be executed by a database engine of the database management system can be determined, and, a second portion of the unified intermediate representation to be executed by a machine learning runtime can be determined.

20210096915, Apr 1, 2021

Nov 27, 2019

16/697960

- Redmond WA, US
Shi QIAO - Bellevue WA, US
Alekh JINDAL - Sammamish WA, US
Malay Kumar BAG - Kirkland WA, US
Rathijit SEN - Madison WI, US
Carlo Aldo CURINO - Woodinville WA, US

G06F 9/50
G06F 9/48
G06N 5/04
G06N 20/00
G06F 16/2453

A serverless query processing system receives a query and determines whether the query is a recurring query or a non-recurring query. The system may predict, in response to determining that the query is the recurring query, a peak resource requirement during an execution of the query. The system may compute, in response to determining that the query is the non-recurring query, a tight resource requirement corresponding to an amount of resources that satisfy a performance requirement over the execution of the query, where the tight resource requirement is less than the peak resource requirement. The system allocates resources to the query based on an applicable one of the peak resource requirement or the tight resource requirement. The system then starts the execution of the query using the resources.

20210089532, Mar 25, 2021

Sep 25, 2019

16/581905

- Redmond WA, US
Rathijit SEN - Madison WI, US
Zhicheng YIN - Kirkland WA, US
Shi QIAO - Bellevue WA, US
Abhishek ROY - Bellevue WA, US
Alekh JINDAL - Sammamish WA, US
Subramaniam Venkatraman KRISHNAN - Santa Clara CA, US
Carlo Aldo CURINO - Woodinville WA, US

G06F 16/2453

The cloud-based query workload optimization system disclosed herein the cloud-based query workloads optimization system receives query logs from various query engines to a cloud data service, extracts various query entities from the query logs, parses query entities to generate a set of common workload features, generates intermediate representations of the query workloads, wherein the intermediate representations are agnostic to the language of the plurality of the queries, identifies a plurality of workload patterns based on the intermediate representations of the query workloads, categorizes the workloads in one or more workload type categories based on the workload patterns and the workload features, and selects an optimization scheme based on the category of workload pattern.

20200265049, Aug 20, 2020

Feb 15, 2019

16/277992

- Redmond WA, US
Konstantinos Karanasos - San Francisco CA, US
Carlo Aldo Curino - Woodinville WA, US

G06F 16/2453
G06F 16/901

Methods, systems, and computer program products are provided for generating and utilizing materialized graph views. A system according to one embodiment includes a graph database including a graph and schema, a workload analyzer, a view enumerator, a query rewriter and an execution engine. The workload analyzer is configured to receive and analyze queries in a query workload. The view enumerator is configured to use an inference engine to operate on facts derived from the graph and a query, and view templates comprising inference rules to enumerate candidate views. The workload analyzer is further configured to selects a candidate view to materialize, provide the selected view to the execution engine that is configured to generate the materialized view. The workload analyzer may select the at least one candidate view based on factors such as query evaluation cost estimates, candidate view performance improvement estimates, view size estimates and view creation cost estimates.

20170134526, May 11, 2017

Jan 20, 2017

15/411641

- Redmond WA, US
Carlo Curino - San Jose CA, US
Kannababu Karanam - Sammamish WA, US
Subramaniam Venkatraman Krishnan - Redmond WA, US
Christopher William Douglas - Mountain View WA, US
Sriram Rao - San Jose CA, US
Mostafa Elhemali - Seattle WA, US
Chuan Liu - Seattle WA, US

H04L 29/08

Embodiments are directed to progressively migrating source computer nodes where the source computer nodes perform a computer-implemented service. In one embodiment, a computer system determines that execution of the performed service is to be migrated from the source computer nodes to target computer nodes. The computer system groups the source computer nodes into multiple source subgroups, where each source subgroup includes at least one source computer node. The computer system then schedules creation of target subgroups of target nodes. These target subgroups include at least one source computer node and, themselves, correspond to a source subgroup. The computer system activates a first target subgroup corresponding to a first source subgroup, and deactivates the first source subgroup. In this manner, the first target subgroup replaces the first source subgroup. Still further, the target subgroups are scheduled to be created only after the first source subgroup has been deactivated.

20150188989, Jul 2, 2015

Dec 30, 2013

14/143797

- Redmond WA, US
Carlo Curino - San Jose CA, US
Kannababu Karanam - Sammamish WA, US
Subramaniam Venkatraman Krishnan - Redmond WA, US
Christopher William Douglas - Mountain View CA, US
Sriram Rao - San Jose CA, US
Mostafa Elhemali - Seattle WA, US
Chuan Liu - Seattle WA, US

Microsoft Corporation - Redmond WA

H04L 29/08

Embodiments are directed to progressively migrating source computer nodes where the source computer nodes perform a computer-implemented service. In one embodiment, a computer system determines that execution of the performed service is to be migrated from the source computer nodes to target computer nodes. The computer system groups the source computer nodes into multiple source subgroups, where each source subgroup includes at least one source computer node. The computer system then schedules creation of target subgroups of target nodes. These target subgroups include at least one source computer node and, themselves, correspond to a source subgroup. The computer system activates a first target subgroup corresponding to a first source subgroup, and deactivates the first source subgroup. In this manner, the first target subgroup replaces the first source subgroup. Still further, the target subgroups are scheduled to be created only after the first source subgroup has been deactivated.