International Business Machines Corporation

Binoy Thomas

Sudheesh S Kairali

Sarbajit Kumar Rakshit

Malarvizhi Kandasamy

Manish Anand Bhide

Swapnil Dongre

Smitha Padmanabhan

Lakshmi K R

Hans W Uhlig

Distributed queries are analyzed to identify repeated queries. Pattern of the repeated queries is learned, where the pattern provides information associated with at least availability of data sources used for responding to the repeated queries and response times of the data sources. For a data source in the data sources, a time window during which the data source has a response time that is slower than a response time criterion is determined; prior to beginning of the time window, data is prefetched from the data source and storing the data in a cache storage; responsive to receiving a distributed query that uses the data source during the time window, the data is fetched from the cache storage, where at least a part of a response to the distributed query is sourced from the cache storage; and after end of the time window, the data is removed from the cache storage.

Distributed query optimization

Aspects include processing the query from a client device to divide the query into a plurality of subqueries, identifying a data source that corresponds to each of the plurality of subqueries, and identifying an expected response time for each of the data sources. Based on a determination that at least one of the expected response times is greater than a default timeout threshold, aspects include classifying the query as a delayed split query and updating a timeout value of the query to be a delayed timeout threshold, which is greater than the default timeout threshold. Aspects also include performing the delayed split execution of the query.

Delayed split execution of a query

An embodiment establishes a transmission performance database that comprises performance data related to transmission of data across a plurality of data channels of a network. The embodiment defines an initial data chunk size for a first data object transmitted across a first data channel of the plurality data channels of the network. The embodiment transmits the first data object across the first data channel according to the initial data chunk size. The embodiment collects performance data for the first data channel. The embodiment evaluates transmission performance of the first data channel based at least in part on the performance data of the first data channel. The embodiment generates a recommendation to adjust the initial data chunk size of the first data object based on the transmission performance of the first data channel. The embodiment adjusts the initial data chunk size based at least in part on the recommendation.

Intelligent data ingestion chunk size optimization

A computer-implemented system and method protect a robot in a hazardous environment. The method comprises detecting, with a hazardous environment detector, the presence of a hazardous gas within a bounded environment defining the boundaries of the hazardous environment, and detecting a wind flow speed and wind flow direction proximate the hazardous environment detector. The method further comprises determining a hazardous gas concentration (HGC) that is at least one of a present HGC and a predicted future HGC, and reading, from a database, a maximum HGC for at least one of the robot and a payload of the robot. Conditioned upon the HGC exceeding a threshold value of the maximum HGC, the method directs the robot to move to a safe location in which the HGC does not exceed the threshold value of the maximum HGC.

Self-protection of robotic vehicles in invisible hazardous gaseous surrounding

A computer-implemented method performs dynamic filtering within a service mesh. While transferring data from one microservice to another microservice, each microservice evaluates a level of trust with each upstream and downstream microservice based on the level of trust score. Each microservice evaluates which data should be filtered while transferring data to downstream microservice and which data should be filtered while receiving data from upstream microservice.

Dynamic data filtering based on trust score within service mesh

A computer-implemented method, according to one embodiment, includes dynamically assigning reputation scores to a plurality of tasks, and dynamically assigning reputation scores to a plurality of edge nodes. The method further includes dynamically placing the tasks into the edge nodes based on the reputation scores assigned to the tasks and the reputation scores assigned to the edge nodes. A computer program product, according to one embodiment, includes a computer readable storage medium having program instructions embodied therewith. The program instructions are readable and/or executable by a computer to cause the computer to perform the foregoing method. A system, according to another embodiment, includes a processor, and logic integrated with the processor, executable by the processor, or integrated with and executable by the processor. The logic is configured to perform the foregoing method.

Dynamically placing tasks into edge nodes based on reputation scores

A method, computer system, and a computer program product are provided for establishing security measures for a content. In one embodiment, the method comprises identifying at least a microservice chain in a network computer. Each microservice chain has more than one microservice linked to one another and each microservice includes a plurality of applications bundled together. Any use profiles associated with the microservice chain are identified and all data entry points into each microservice are determined. Each microservice is analyzed to predict when each microservice in the chain will provide a confidential output. Once any microservice chain is deemed to be providing a confidential output, any predicted exits in the microservice deemed to be generating an output content are upgraded so that all predicted exists provide a confidential output.

Providing dynamic network security based on importance of proprietary content

In an approach for managing data continuity of a data storage system, a processor analyzes queries received by the data storage system from a plurality of data sources. A processor identifies a query frequency and response time for queries from each data source of the plurality of data sources. A processor derives an expected data pipeline continuity requirement for each data source based on respective query frequency and respective response time for the respective queries from each data source. A processor learns patterns of the analyzed queries to enable predictions for when future queries from each data source will occur and identification of an unavailability pattern for each data source. A processor determines recommendations for maintaining data continuity for the data storage system based on the expected data pipeline continuity requirement for each data source, the query predictions, and the unavailability pattern for each data source.

Managing continuity in data pipeline

Provided are a computer program product, system, and method for rendering an extended-reality representation of a virtual meeting. An extended reality representation is generated of a virtual meeting including avatars associated with participants in the virtual meeting to render in extended-reality displays of the participants. An extended-reality representation of a virtual controller corresponding to a real-world controller in a real-world location is generated to render in at least one extended-reality display of at least one participant in the virtual meeting. Participant input is received from one of the at least one participant to control the virtual controller. The participant input is translated to control the virtual controller to commands to control the real-world controller. The commands are transmitted to the real-world controller at the real-world location to control the real-world controller to perform actions in the real-world location to carry out the transmitted commands.

Rendering an extended-reality representation of a virtual meeting including virtual controllers corresponding to real-world controllers in a real-world location

Data relevancy-based data retention is provided. Data is assigned to a logical data retention relevancy compartment of a plurality of logical data retention relevancy compartments in a data lakehouse. It is determined whether a data retention relevancy value of the data is greater than a data retention threshold level of a data retention policy corresponding to the logical data retention relevancy compartment storing the data. The data is transferred from a physical data storage unit corresponding to the logical data retention relevancy compartment of the plurality of logical data retention relevancy compartments in the data lakehouse to an archive in response to determining that the data retention relevancy value of the data is greater than the data retention threshold level of the data retention policy corresponding to the logical data retention relevancy compartment storing the data.

Data relevance-based data retention in data lakehouses

In an approach to incremental clustering of continuous flow data streams to preserve data continuity, one or more computer processors receive a definition of a first major cluster period and a definition of a first minor cluster period. One or more computer processors receive a continuous stream of data. One or more computer processors cluster the continuous stream of data, based on the definition of the first major cluster period and on the definition of the first minor cluster period. One or more computer processors determine a similarity between a first data cluster and a second data cluster, wherein the second data cluster occurs after the first minor cluster period. One or more computer processors determine whether the similarity meets a pre-defined minimum threshold of similarity. One or more computer processors increase the first minor cluster period to a second minor cluster period.

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Kozhikode, India

Binoy Thomas