Abstract: A system that maps files into multiple parts and initiates storage of each part within a respective storage provider. The mapping facilitates this by correlating each of the multiple parts to a corresponding storage provider which may be different than the storage provider that stores a different part of the same file. The system then initiates storage of the multiple parts of the file in each of their corresponding storage provider in accordance with the mapping, and using a storage-provider-facing API. Similarly, retrieval and/or editing of a partial file may be accomplished by referencing the mapping, and using the same API issued to the appropriate storage provider. Each storage provider is capable of interfacing with the system using this same API.

Abstract: 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.

Abstract: Operating upon encrypted data with a particular data scope. A base encryption key is established and associated with the particular data scope, and then stored in a base encryption key store. That base encryption key store might be managed by an application or service that stores base encryption keys for multiple data scopes. A proxy encryption key acts as a kind of proxy for the base encryption key. The proxy encryption key may be used for frequent operations on encrypted data within the particular data scope. Thus, the principles described herein act as a frequency amplifier that allows key-based operations upon the particular data scope to be performed at much higher frequencies than otherwise would be possible by operating directly using the base encryption key.

Abstract: Sharding a data unit across storage locations (e.g., data centers, clusters, accounts, and so forth). For each location, the multi-link traffic constraints in data channeling with the storage location are identified. For instance, for a given data channel leading to and/or from a storage location, there may be multiple links. Each link may have its own constraints (e.g., in throughput and/or storage). Policy may consider the sharding of files in a manner that honors the constraints. The policy may also be set so that all portions of the shards may be timely accessed in parallel. When sharding, the policy is applied in order to identify initial placement of the computing data unit across the multiple storage locations. Furthermore, the policy may be consulted to determine which storage location to access the shard from and/or which channel to use to access the shard from a given location.

Abstract: Operating upon encrypted data with a particular data scope. A base encryption key is established and associated with the particular data scope, and then stored in a base encryption key store. That base encryption key store might be managed by an application or service that stores base encryption keys for multiple data scopes. A proxy encryption key acts as a kind of proxy for the base encryption key. The proxy encryption key may be used for frequent operations on encrypted data within the particular data scope.

Abstract: Operating upon encrypted data with a particular data scope. A base encryption key is established and associated with the particular data scope, and then stored in a base encryption key store. That base encryption key store might be managed by an application or service that stores base encryption keys for multiple data scopes. A proxy encryption key acts as a kind of proxy for the base encryption key. The proxy encryption key may be used for frequent operations on encrypted data within the particular data scope. Thus, the principles described herein act as a frequency amplifier that allows key-based operations upon the particular data scope to be performed at much higher frequencies than otherwise would be possible by operating directly using the base encryption key.

Abstract: Aspects extend to methods, systems, and computer program products for optimally pipelining result sets with fault tolerance in distributed query execution. Distributed computing jobs are optimized by dividing the distributed computing jobs into one or more bubbles for execution. Each bubble can be independently executed, potentially in parallel with other bubbles, when resources to handle the bubble are available. Intra-bubble communication can be streamed between vertices within a bubble. Inter-bubble communication can be stored to durable storage. Bubbles provide a failure boundary for a job graph and re-executing a bubble along with storage of intermediate results in durable storage can be used to recover from failures. When a vertex inside a bubble fails, computation can resume by rescheduling the execution of the failed bubble from the durable inputs for that bubble. Durable storage provides a light-weight failover to handle non-deterministic behavior.

Abstract: Sharding a data unit across storage locations (e.g., data centers, clusters, accounts, and so forth). For each locations, the multi-link traffic constraints in data channeling with the storage location are identified. For instance, for a given data channel leading to and/or from a storage location, there may be multiple links. Each link may have their own constraints (e.g., in throughput and/or storage). Policy may consider the sharding of files in a manner that honors the constraints. The policy may also be set so that all portions of the shards may be timely accessed in parallel. When sharding, the policy is applied in order to identify initial placement of the computing data unit across the multiple storage locations. Furthermore, the policy may be consulted to determine which storage location to access the shard from and/or which channel to use to access the shard from a given location.

Abstract: Operating upon encrypted data with a particular data scope. A base encryption key is established and associated with the particular data scope, and then stored in a base encryption key store. That base encryption key store might be managed by an application or service that stores base encryption keys for multiple data scopes. A proxy encryption key acts as a kind of proxy for the base encryption key. The proxy encryption key may be used for frequent operations on encrypted data within the particular data scope. Thus, the principles described herein act as a frequency amplifier that allows key-based operations upon the particular data scope to be performed at much higher frequencies than otherwise would be possible by operating directly using the base encryption key.

Abstract: A system that maps files into multiple parts and initiates storage of each part within a respective storage provider. The mapping facilitates this by correlating each of the multiple parts to a corresponding storage provider which may be different than the storage provider that stores a different part of the same file. The system then initiates storage of the multiple parts of the file in each of their corresponding storage provider in accordance with the mapping, and using a storage-provider-facing API. Similarly, retrieval and/or editing of a partial file may be accomplished by referencing the mapping, and using the same API issued to the appropriate storage provider. Each storage provider is capable of interfacing with the system using this same API.

Abstract: Aspects extend to methods, systems, and computer program products for optimally pipelining result sets with fault tolerance in distributed query execution. Distributed computing jobs are optimized by dividing the distributed computing jobs into one or more bubbles for execution. Each bubble can be independently executed, potentially in parallel with other bubbles, when resources to handle the bubble are available. Intra-bubble communication can be streamed between vertices within a bubble. Inter-bubble communication can be stored to durable storage. Bubbles provide a failure boundary for a job graph and re-executing a bubble along with storage of intermediate results in durable storage can be used to recover from failures. When a vertex inside a bubble fails, computation can resume by rescheduling the execution of the failed bubble from the durable inputs for that bubble. Durable storage provides a light-weight failover to handle non-deterministic behavior.

Abstract: Content items are selected to be displayed on a portal page in such a way as to maximize a performance metric such as click-through rate. Problems relating to content selection are addressed, such as changing content pool, variable performance metric, and delay in receiving feedback on an item once the item has been displayed to a user. An adaptation of priority-based schemes for the multi-armed bandit problem, are used to project future trends of data. The adaptation introduces experiments concerning a future time period into the calculation, which increases the set of data on which to solve the multi-armed bandit problem. Also, a Bayesian explore/exploit method is formulated as an optimization problem that addresses all of the issues of content item selection for a portal page. This optimization problem is modified by Lagrange relaxation and normal approximation, which allow computation of the optimization problem in real time.

Abstract: In one embodiment, for each one of one or more content producers, access a content-producing rate at which the content producer produces one or more items of content; and for each one of one or more content consumers, access a content-consuming rate at which the content consumer consumes one or more items of content. For each one of a plurality of consumer-producer pairs, wherein the consumer-producer pair comprises one of the content consumers and one of the content producers, and the content consumer follows the content producer, select between a push strategy and a pull strategy for delivering one or more items of content from the content producer to the content consumer based on the content-consuming rate of the content consumer and the content-producing rate of the content producer.

Abstract: Content items are selected to be displayed on a portal page in such a way as to maximize a performance metric such as click-through rate. Problems relating to content selection are addressed, such as changing content pool, variable performance metric, and delay in receiving feedback on an item once the item has been displayed to a user. An adaptation of priority-based schemes for the multi-armed bandit problem, are used to project future trends of data. The adaptation introduces experiments concerning a future time period into the calculation, which increases the set of data on which to solve the multi-armed bandit problem. Also, a Bayesian explore/exploit method is formulated as an optimization problem that addresses all of the issues of content item selection for a portal page. This optimization problem is modified by Lagrange relaxation and normal approximation, which allow computation of the optimization problem in real time.

Abstract: Content items are selected to be displayed on a portal page in such a way as to maximize a performance metric such as click-through rate. Problems relating to content selection are addressed, such as changing content pool, variable performance metric, and delay in receiving feedback on an item once the item has been displayed to a user. An adaptation of priority-based schemes for the multi-armed bandit problem are used to project future trends of data. The adaptation introduces experiments concerning a future time period into the calculation, which increases the set of data on which to solve the multi-armed bandit problem. Also, a Bayesian explore/exploit method is formulated as an optimization problem that addresses all of the issues of content item selection for a portal page. This optimization problem is modified by Lagrange relaxation and normal approximation, which allow computation of the optimization problem in real time.

Abstract: In one embodiment, for each one of one or more content producers, access a content-producing rate at which the content producer produces one or more items of content; and for each one of one or more content consumers, access a content-consuming rate at which the content consumer consumes one or more items of content. For each one of a plurality of consumer-producer pairs, wherein the consumer-producer pair comprises one of the content consumers and one of the content producers, and the content consumer follows the content producer, select between a push strategy and a pull strategy for delivering one or more items of content from the content producer to the content consumer based on the content-consuming rate of the content consumer and the content-producing rate of the content producer.

Abstract: In one embodiment, for each one of one or more content producers, access a content-producing rate at which the content producer produces one or more items of content; and for each one of one or more content consumers, access a content-consuming rate at which the content consumer consumes one or more items of content. For each one of a plurality of consumer-producer pairs, wherein the consumer-producer pair comprises one of the content consumers and one of the content producers, and the content consumer follows the content producer, select between a push strategy and a pull strategy for delivering one or more items of content from the content producer to the content consumer based on the content-consuming rate of the content consumer and the content-producing rate of the content producer.

Abstract: In one embodiment, for each one of one or more content producers, access a content-producing rate at which the content producer produces one or more items of content; and for each one of one or more content consumers, access a content-consuming rate at which the content consumer consumes one or more items of content. For each one of a plurality of consumer-producer pairs, wherein the consumer-producer pair comprises one of the content consumers and one of the content producers, and the content consumer follows the content producer, select between a push strategy and a pull strategy for delivering one or more items of content from the content producer to the content consumer based on the content-consuming rate of the content consumer and the content-producing rate of the content producer.

Abstract: Content items are selected to be displayed on a portal page in such a way as to maximize a performance metric such as click-through rate. Problems relating to content selection are addressed, such as changing content pool, variable performance metric, and delay in receiving feedback on an item once the item has been displayed to a user. An adaptation of priority-based schemes for the multi-armed bandit problem are used to project future trends of data. The adaptation introduces experiments concerning a future time period into the calculation, which increases the set of data on which to solve the multi-armed bandit problem. Also, a Bayesian explore/exploit method is formulated as an optimization problem that addresses all of the issues of content item selection for a portal page. This optimization problem is modified by Lagrange relaxation and normal approximation, which allow computation of the optimization problem in real time.