International Business Machines Corporation

Xiaoyang Yang

Jing James Xu

Yuan Yuan Li

Jun Song Wang

Ji Hui Yang

Hai Ji

Chun Guang Zeng

Li Fu Chen

Jun Wei Zhang

Lei Chen

Alan T Yaung

James Allen Reimer

Guang Ming Zhang

Hong Wei Jia

Mo Chi Liu

Yun Bj Wang

Yun Ting Wang

Wu Yan

He Yang

Embodiments of the present disclosure relate to a solution for translation verification and correction. According to the solution, a neural network is trained to determine an association degree among a group of words in a source or target language. The neural network can be used for translation verification and correction. According to the solution, a group of words in a source language and translations of the group of words in a target language are obtained. An association degree among the group of words and an association degree among the translations can be determined by using the trained neural network. Then, whether there is a wrong translation can be determined based on the association degrees. In some embodiments, corresponding methods, systems and computer program products are provided.

Translation verification and correction

A method of operating a shared computing environment includes receiving a client request for an optimized runtime configuration of the shared computing environment for executing a task, receiving runtime data of the task collected from a prior runtime, inferring a runtime configuration from the runtime data, determining a predicted timeseries workload for the shared computing environment, updating the runtime configuration to including the timeseries workload prediction, wherein the updating outputs the optimized runtime configuration, and configuring the shared computing environment according to the optimized runtime configuration in executing the task.

Machine learning based runtime optimization

Provided are techniques for optimized content object storage service for large scale content. A content object file is created. An index entry for the content object file is created with a content object key and a content object location. The content object file is appended to an aggregated file on a storage node. In response to a request to retrieve the content object file from the aggregated file, the content object key is used to access the content object location that describes the storage node, a name of the aggregated file, an offset into the aggregated file, and a size of the content object file to retrieve the content object file.

Optimized content object storage service for large scale content

Embodiments generally relate to determining traffic congestion patterns. In some embodiments, a method includes identifying congestion events for each road of a plurality of roads in a road network, where each congestion event indicates a drop in average vehicle speed below a predetermined speed threshold for a particular road in the road network, and where the congestion events span a predetermined time period. The method further includes determining local clusters of the congestion events based on one or more road condition parameters, where each local cluster defines a local congestion pattern for a particular road of the plurality of roads in the road network. The method further includes grouping the local clusters into one or more global clusters based on the one or more road condition parameters, where the global clusters define global congestion patterns in the road network.

Determining traffic congestion patterns

A latest status record of an object in a local storage node is obtained as a local status record of the object. The status record includes a name of an operation changing the object, a timestamp of the operation changing the object, a first status flag, and a second status flag. The first status flag indicates whether a synchronization of the object from current object storage node to another object storage node is executed. The second status flag indicates whether a synchronization of the object from another object storage node to the current object storage node is executed. The latest status record of the object is obtained of the object in from a remote object storage node as a remote status record of the object. A synchronization of the object is executed based on the local status record and the remote status record.

Synchronizing object in local object storage node

A method, computer system, and a computer program product for detecting a plurality of influential factors for traffic congestion is provided. The present invention may include identifying one or more influential roads and one or more influential routings associated with an object road in a target road network. The present invention may also include quantifying an impact associated with the identified one or more influential roads and the identified one or more influential routings associated with the object road, wherein at least one Impact Matrix is utilized to determine a road-by-road influence in connection to the target road network.

Detecting influential factors for traffic congestion

A method and system for storage of an aggregation file and method and system for compression of the same. The method for compressing an aggregation file includes: acquiring the aggregation file to be compressed; copying remaining files in the acquired aggregation file into a new aggregation file based on metadata of a deleted object stored in a deletion file corresponding to the acquired aggregation file; and removing the acquired aggregation file. The present invention also provides a system for compressing an aggregation file and a method and system for storing an aggregation file.

Storage and compression of an aggregation file

A method, computer program product, and computer system for disk management in a distributed storage system. The distributed storage system comprises a plurality of disks within a main disk ring, where the disks store target data. In one embodiment, the method comprises dividing the target data into cold target data and hot target data, and grouping one or more disks within the main disk ring into a cold data disk ring and the remaining one or more disks within the main disk ring into a hot data disk ring. The method further comprises migrating the cold target data on disks not within the cold data disk ring onto disks within the cold data disk ring while migrating the hot target data on disks not within the hot data disk ring onto disks within the hot data disk ring, and reducing a spinning rate of disks within the cold data disk ring.

Disk management in distributed storage system including grouping disks into cold and hot data disk rings and reducing a spinning rate of disks storing cold data

A method, computer program product, and computer system are disclosed for disk management in a distributed storage system, wherein the distributed storage system comprises a plurality of disks within a main disk ring, and the disks store target data. In one embodiment, the method comprises dividing the target data into cold target data and hot target data, and grouping one or more disks within the main disk ring into a cold data disk ring and the remaining one or more disks within the main disk ring into a hot data disk ring, based on the cold target data's and the hot target data's positions on disks. The method further comprises migrating the cold target data on disks not within the cold data disk ring onto disks within the cold data disk ring while migrating the hot target data on disks not within the hot data disk ring onto disks within the hot data disk ring, and reducing a spinning rate of disks within the cold data disk ring.

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San Francisco, CA, United States of America

Xiaoyang Yang