Microsoft Technology Licensing, LLC

Alibaba Damo (hangzhou) Technology Co., Ltd.

Jingren Zhou

Per-Ake Larson

Nicolas Bruno

Wei Lin

Eric Boutin

Jaliya Nishantha Ekanayake

Haibo Lin

Peter Zabback

Xiaoyu Chen

Zhicheng Yin

Zhengping Qian

Tao Guan

Paul Michael Brett

Nan Zhang

Anna Korsun

Pavel N Iakovenko

Jonathan David Goldstein

Srikanth Kandula

Patrick James Helland

Weizhu Chen

Ronnie Chaiken

Jonathan A Forbes

Guido Moerkotte

Peter Alan Carlin

Hiren Shantilal Patel

Jie Liu

Bolin Ding

Thomas Phan

Bin Shi

Ming-Chuan Wu

Fei Xu

Edward John Triou, Jr

Chang Zhou

Yaron Burd

David Edward DeHann

Huiling Zhou

Shuai Bai

Zhikang Li

Jinbao Xue

Hongxia Yang

Zitao Li

Sameer Agarwal

Pre-Ake Larson

Frank W Tompa

Runtime statistics from the actual performance of operations on a set of data are collected and utilized to dynamically modify the execution plan for processing a set of data. The operations performed are modified to include statistics collection operations, the statistics being tailored to the specific operations being quantified. Optimization policy defines how often optimization is attempted and how much more efficient an execution plan should be to justify transitioning from the current one. Optimization is based on the collected runtime statistics but also takes into account already materialized intermediate data to gain further optimization by avoiding reprocessing.

Continuous cloud-scale query optimization and processing

An item recommendation method, a device and a learning system for protecting user privacy are provided. The method includes: obtaining a locally collected scoring matrix, the scoring matrix being used to describe a scoring situation of multiple users on multiple items; locally training an item expression matrix and a user expression matrix corresponding to the scoring matrix, and sending a first user expression matrix obtained by local training to a server, so that the server aggregates and processes user expression matrices independently uploaded by the multiple data devices, and sends an aggregated second user expression matrix to the multiple data devices; and predicting degree of preferences of the multiple users for the multiple items according to the item expression matrix obtained by the local training and the second user expression matrix, so that item recommendations are performed for the multiple users according to the degrees of preference.

Item recommendation method and device for protecting user privacy and learning system

Embodiments of this application provide an image generation system and method. In an exemplary manufacturing industry scenario, a style requirement of a product category in a manufacturing industry is automatically captured according to user behavior data and product description information associated with the product category. Based on these data, a style description text may be generated and converted to product images by using a text prediction-based image generation model. The product images are further screened by using an image-text matching model, to obtain a product image with high quality. This process covers from style description text mining to text-to-image prediction to image quality evaluation. It provides an automation product image generation capability for the manufacturing industry, shorten a cycle of designing and producing the product image in the manufacturing industry, and improve production efficiency of the product image.

Image generation system and method

A low-latency cloud-scale computation environment includes a query language, optimization, scheduling, fault tolerance and fault recovery. An event model can be used to extend a declarative query language so that temporal analysis of event of an event stream can be performed. Extractors and outputters can be used to define and implement functions that extend the capabilities of the event-based query language. A script written in the extended query language can be translated into an optimal parallel continuous execution plan. Execution of the plan can be orchestrated by a streaming job manager which schedules vertices on available computing machines. The streaming job manager can monitor overall job execution. Fault tolerance can be provided by tracking execution progress and data dependencies in each vertex. In the event of a failure, another instance of the failed vertex can be scheduled. An optimal recovery point can be determined based on checkpoints and data dependencies.

Distributed stream processing in the cloud

Processing a job request for multiple versions of a distributed computing service. The service processing node does this by at least interleavingly (e.g., via time sharing with rapid context switching, or by actually concurrently) running a first runtime library associated with a first service version of the distributed computerized service and a second runtime library associated with a different service version of the distributed computerized service. While running the first runtime library, job requests of a first service version may be at least partially processed using a first set of one or more executables that interact with the first runtime library. While running the second runtime library, job requests of a second service version may be at least partially processed using a second set of one or more executables that interact with the second runtime library.

Processing requests for multi-versioned service

Embodiments of the present invention allow multiple data streams to be analyzed as a single data set. The single data set may be described as a stream set herein. The multiple streams that are included in the stream set may be specified through a user script or query. For example, a query may be used to gather all streams created within a date range. The query could include one or more filters to gather certain information from the data streams or to exclude certain data streams that otherwise are in the query's range. A stream may be an unstructured byte stream of data. The stream may be created by append-only writing to the end of the stream. The stream could also be a structured stream that includes metadata that defines column structure and affinity/clustering information.

Analyzing multiple data streams as a single data object

Processing received job requests for a multi-versioned distributed computerized service. For each received job request, the job request is channeled to an appropriate service processing node that depends on the version of the distributed computing service that is to handle the job request. A version of the distributed computing service is assigned to the incoming job request. A service processing node that runs a runtime library for the assigned service version is then identified. The identified service processing node also has an appropriate set of one or more executables that allows the service processing node to plan an appropriate role (e.g., compiler, scheduler, worker) in the distributed computing service. The job request is then dispatched to the identified service processing node.

Processing request for multi-versioned service

Distributed stream processing in the Cloud

A method for machine learning a data set in a data processing framework is disclosed. A forest is trained with the data set that generates a plurality of trees in parallel. Each tree includes leaf nodes having a constant weight. A discriminative value for each leaf node is learned with a supervised model. The forest is reconstructed with the discriminative values replacing the constant weight for each leaf node.

Distributed stage-wise parallel machine learning

A job scheduler that schedules ready tasks amongst a cluster of servers. Each job might be managed by one scheduler. In that case, there are multiple job schedulers which conduct scheduling for different jobs concurrently. To identify a suitable server for a given task, the job scheduler uses expected server performance information received from multiple servers. For instance, the server performance information might include expected performance parameters for tasks of particular categories if assigned to the server. The job management component then identifies a particular task category for a given task, determines which of the servers can perform the task by a suitable estimated completion time, and then assigns based on the estimated completion time. The job management component also uses cluster-level information in order to determine which server to assign a task to.

Job scheduling using expected server performance information

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Bellevue, WA, United States of America

Jingren Zhou