Amazon Technologies, Inc.

Microsoft Technology Licensing, LLC

Sas Institute Inc.

Janssen Pharmaceutica Nv

Wei Xiao

Stefano Stefani

Timothy Andrew Rath

Stuart Henry Seelye Marshall

Miguel Mascarenhas Filipe

Yijun Lu

Kiran-Kumar Muniswamy-Reddy

Maximiliano Maccanti

David Alan Lutz

Bjorn Patrick Swift

Rande Anthony Blackman

David Craig Yanacek

Jai Vasanth

Xianglong Huang

Rajaprabhu Thiruchi Loganathan

Clarence Wing Yin Ng

Akshat Vig

Pejus Manoj Das

James Robert Hamilton

Jakub Kulesza

Rama Krishna Sandeep Pokkunuri

William Alexander Stevenson

Srivaths Badrinath Copparam

Swaminathan Sivasubramanian

Chiranjeeb Buragohain

Kiran Kumar Muniswamy Reddy

Somasundaram Perianayagam

Adam Douglas Morley

Raymond S Bradford

Usman Ahmed Shami

Yan Valerie Leshinsky

Tengiz Kharatishvili

Arin Chaudhuri

Grant A M McAlister

Fahad Ahmed

Jiandan Zheng

Lokendra Singh Panwar

Chiranjeeb Buragahain

Andrew Desmond Budiman

Grant Alexander Macdonald McAlister

J Guy Breitenbucher

Jorge Manuel Gomes Da Silva

Sameet Harishanker Agarwal

Richard Apodaca

Sharatkumar Nagesh Kuppahally

Mark Seierstad

Ravi Math

Mohammad Zeeshan Qureshi

Saba Emrani

Shirley Xue Yi Ly

Tate Andrew Certain

James Christopher Sorenson

Matthew Shawn Wilson

Sudipta Sengupta

Neelakandha S Mani

Rahul Sharma

Chandravadan R Shah

Nigel Robin Ellis

Satish Ramchandra Thatte

Bing Xiang

Devin M Swanson

John M Keith

Vladimir Pogrebinsky

Qiang Liu

Natalie Anne Hawryluk

Xiaohu Deng

Kiev S Ly

Poorna Chand Srinivas Perumalla

Medhavi Dhawan

Vaibhav Jain

Girish Mittur Venkataramanappa

Akhilesh Mritunjai

Ramesh M Nallapati

Rodger N Kline

Jill A Jablonowski

Deovrat Vijay Kakde

Barry Bailey Hunter, Jr

Jeffrey E Merit

Ming-Chuan Wu

Alexander Richard Keyes

Siddharth Vivek Joshi

Eddie Fusaro

Atul Deo

Ganapathy Subramaniam Krishnamoorthy

Andrew Oliver Arnold

Alison L Chambers

Arunachallam Sivaprakasam Sivakumar

Sergiy Peredriy

Kanaka Pattabiraman

Haifeng He

Mohamed Farouk AbdelHady

Hansi Jiang

Henghui Zhu

Anuroop Arora

Michael I Zwilling

Aanchal Gupta

Xiaofei Ma

Seung Hyun Kong

Dejiao Zhang

Joan M Pierce

A system that implements a scaleable data storage service may maintain tables in a data store on behalf of storage service clients. The service may maintain data in partitions stored on respective computing nodes in the system. The service may support multiple throughput models, including a committed throughput model and a best effort throughput model. A service request to create a table may specify that requests directed to the table should be serviced under a committed throughput model and may specify the committed throughput level in terms of logical service request units. The service may reserve low-latency storage and other resources sufficient to meet the specified committed throughput level. A client/user may request a modification to the committed throughput level in anticipation of workload changes, such as an increase or decrease in traffic or data volume. In response, the system may increase or decrease the resources reserved for the table.

System and method for providing a committed throughput level in a data store

Systems and methods develop and apply one or more extractive summarization models for locating contact center conversation details in a transcript, extracting pertinent verbiage, and, in a transformation of the communication details, automatically generating summaries at one or more levels of abstraction, the summaries in full sentences, in a manner that a contact center agent understands. The models are trained using machine learning algorithms.

Systems and methods for automated communication summarization

Techniques for selecting a skill to execute in response to a natural language input are described. A system may receive a natural language input, determine profile data associated with the natural language input, and determine the profile data indicates a locale and at least first language and second languages. The system determines first and second sets of skills corresponding to the locale/first language and locale/second language, respectively. The system determines a first group of skill candidates corresponding to a portion of the first set of skills, and determines a second group of skill candidates corresponding to a portion of the second set of skills. The system performs ranking processing across the first and second groups of skills to determine a best skill for responding to the natural language input. Thus, in some situations, the skill invoked may not correspond to the same language represented in the natural language input.

Skill selection for responding to natural language inputs

Consistent queries to replicated data may be performed according to global clock values. Time values for updates performed at a data set may be identified according to a global clock. The updates may be replicated to a replicated portion of a data set. Queries directed to the replicated portion of the data set may be performed in response to determining that the time values for updates indicate that the replicated portion of the data set is consistent with a time value identified for the query according to the global clock.

Global clock values for consistent queries to replicated data

A system that implements a data storage service may store data for a database table in multiple replicated partitions on respective storage nodes. In response to a request to back up a table, the service may back up individual partitions of the table to a remote storage system independently and (in some cases) in parallel, and may update (or create) and store metadata about the table and its partitions on storage nodes of the data storage service and/or in the remote storage system. Backing up each partition may include exporting it from the database in which the table is stored, packaging and compressing the exported partition for upload, and uploading the exported, packaged, and compressed partition to the remote storage system. The remote storage system may be a key-value durable storage system in which each backed-up partition is accessible using its partition identifier as the key.

Backup of partitioned database tables

Distributed database management systems may maintain collections of items spanning multiple partitions. Index structures may correspond to items on one partition or to items on multiple partitions. Item collections and indexes may be replicated. Changes to the data maintained by the distributed database management system may result in updates to multiple index structures. The changes may be compiled into an instruction set applicable to the index structures. In-memory buffers may contain the instructions prior to transmission to affected partitions. Replication logs may be combined with an acknowledgment mechanism for reliable transmission of the instructions to the affected partitions.

Index update pipeline

A system that implements a scaleable data storage service may maintain tables in a non-relational data store on behalf of clients. Each table may include multiple items. Each item may include one or more attributes, each containing a name-value pair. Attribute values may be scalars or sets of numbers or strings. The system may provide an API usable to request that values of one or more of an item's attributes be updated. An update request may be conditional on expected values of one or more item attributes (e.g., the same or different item attributes). In response to a request to update the values of one or more item attributes, the previous values and/or updated values may be optionally returned for the updated item attributes or for all attributes of an item targeted by an update request. Items stored in tables may be indexed using a simple or composite primary key.

System and method for conditionally updating an item with attribute granularity

A system that implements a scalable data storage service may maintain tables in a data store on behalf of storage service clients. The service may maintain table data in multiple replicas of partitions that are stored on respective computing nodes in the system. In response to detecting an anomaly in the system, detecting a change in data volume on a partition or service request traffic directed to a partition, or receiving a service request from a client to split a partition, the data storage service may create additional copies of a partition replica using a physical copy mechanism. The data storage service may issue a split command defined in an API for the data store to divide the original and additional replicas into multiple replica groups, and to configure each replica group to maintain a respective portion of the table data that was stored in the partition before the split.

System and method for performing live partitioning in a data store

A system that provides services to clients may receive and service requests, various ones of which may require different amounts of work. The system may determine whether it is operating in an overloaded or underloaded state based on a current work throughput rate, a target work throughput rate, a maximum request rate, or an actual request rate, and may dynamically adjust the maximum request rate in response. For example, if the maximum request rate is being exceeded, the maximum request rate may be raised or lowered, dependent on the current work throughput rate. If the target or committed work throughput rate is being exceeded, but the maximum request rate is not being exceeded, a lower maximum request rate may be proposed. Adjustments to the maximum request rate may be made using multiple incremental adjustments. Service request tokens may be added to a leaky token bucket at the maximum request rate.

System and method for throttling service requests having non-uniform workloads

Techniques for active learning-based data labeling are described. An active learning-based data labeling service enables a user to build and manage large, high accuracy datasets for use in various machine learning systems. Machine learning may be used to automate annotation and management of the datasets, increasing efficiency of labeling tasks and reducing the time required to perform labeling. Embodiments utilize active learning techniques to reduce the amount of a dataset that requires manual labeling. As subsets of the dataset are labeled, this label data is used to train a model which can then identify additional objects in the dataset without manual intervention. The label data can be added to an augmented manifest, the augmented manifest can be used to filter the dataset to perform further labeling jobs on the same or different subsets of the dataset.

Active learning-based data labeling service using an augmented manifest

A distributed data storage system may implement determining indexing progress for a table stored in the distributed data storage system. A table may be stored in multiple table partitions. When a secondary index is created for the table, each partition may independently index the items stored within the table partition in order to identify those items in the partition that should be stored in the secondary index. During creation of the secondary index, creation progress points that indicate the progress of the indexing across the table partitions may be determined. The creation progress points may be provided via a programmatic, textual, or graphical interface to the distributed data storage system.

Determining indexing progress for a table in a distributed data store

A system that implements a data storage service may store data for database tables in multiple replicated partitions on respective storage nodes. In response to a request to back up a table, the service may export individual partitions of the table from the database and package them to be independently uploaded (e.g., in parallel) to a remote storage system (e.g., a key-value durable storage system). Prior to uploading the exported and packaged partitions to the remote storage system, the service may verify that the exported and packaged partitions can be subsequently restored, which may include unpackaging and/or re-inflating the exported and packaged partitions to create additional unpackaged copies of the partitions, re-importing the additional unpackaged copies of the partitions into the database (e.g., as additional replicas), and/or comparing checksums generated for the exported partitions with checksums generated for the additional unpackaged copies of the partitions.

Verification of database table partitions during backup

One or more table partitions may communicate with an index partition that may be a master of a replication group. A communications channel may exist between table partitions and the index partition. Upon splitting the index partition, communications between the table partitions and the index partition may be suspended. Upon completion of the split, communications may be reestablished between the table partitions and a partition, of the replication group of index partitions, designated to be a master following the split. Messages accumulated by the table partitions during the split may be sent to the index partition upon reestablishing communications.

Table and index communications channels

Scalable architecture for propagating updates may be implemented for data replicated from a data set. A node may receive updates to items in a data set that have been committed to the data set. The node may determine whether the update should be applied to a replicated portion of the data set. For updates that should be applied, the node may identify another node that hosts the replicated portion of the data set and send a request to the other node to perform a conditional atomic operation to apply the update to the item in the replicated portion of the data set. The condition may compare a version identifier associated with an update and a current version identifier for the item at the other node. If the condition evaluates true, then the update to the item in the replicated portion may be performed.

Scalable architecture for propagating updates to replicated data

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Wei Xiao