International Business Machines Corporation

Kyndryl, Inc.

International Business Corporation

Thiago Bianchi

Tiago Bertoni Scarton

Joao Souto Maior

Tarcisio Pereira

Thiago Roberto Fontes Iunes

Marlon Nascimento Vicente

John Donald Vasquez

Pablo Roberto Millicay Gonzalez

Giuliano Diniz De Morais

Eduardo Dias Felicio Junior

Ana Paula Brambila

Mudhakar Srivatsa

Marco Aurelio Stelmar Netto

John Maxwell Cohn

Raghu Kiran Ganti

Sergio Varga

Igor Monteiro Vieira

Marcelo Mota Manhaes

Fabio Zanforlin Zaina

Leonardo De Bem Ferreira

Silvana Galeazzi

Dashiell Lucena Gonzaga Borges

Marco Antonio Milanez

Marcos Eugenio Fernandes

Adriana Melges Quintanilha Weingart

Marcelo De Souza Vaz

Gary A Cooper

Suellen Caroline Da Silva

Suraj Shirodkar Singh

A method for predicting a time to a failure condition of an asset includes a first model, trained to reconstruct input image pairs to resemble a healthy condition asset. Similarity coefficients are generated for respective historical image pairs that include a visible-light and infrared light image by use of the reconstructed image pair by the first model as a similarity base and the respective historical image pairs include a timestamp of image capture. A second model is trained to predict a time to a failure condition of the asset based on similarity coefficients and timestamps of real-time image pair capture, timestamps of an asset failure condition, and similarity coefficients of the respective historical images. Responsive to receipt of a first real-time image pair, the method computes a predicted time to the failure condition of the asset.

Asset maintenance prediction using infrared and regular images

Systems and methods for automated mainframe database maintenance are provided. In implementations, a method includes obtaining, by a computing device, real-time performance metrics of a mainframe database; automatically generating, by the computing device, a predicted maintenance task as an output of a trained database maintenance task classification machine learning (ML) model based on an input of the real-time performance metrics; automatically generating, by the computing device, a time to execute the predicted maintenance task as an output of a trained database maintenance triggering ML model based on an input of the predicted maintenance task and the real-time performance metrics; automatically generating, by the computing device, maintenance task instructions for the mainframe database based on the predicted maintenance task, the time to execute the predicted maintenance task, and a maintenance profile of the mainframe database; and automatically initiating, by the computing device, the execution of the maintenance task instructions.

Automated mainframe database maintenance

Provided are a computer-implemented method, a computer program product, and a computer system for drone deployment for distributed asset maintenance and repair. Embodiments identify a fix for a problem at an asset and identify a drone to perform the fix. Embodiments generate an initial flight plan that describes a drone flight path for the drone, and embodiments generate an updated flight plan for the drone by updating the drone flight path using real-time air traffic data, real-time road traffic data, and real-time drone flight path conditions obtained from one or more edge devices. Embodiments generate an overall flight plan for the drone and one or more other drones using a predicted cost and a predicted period of time for the updated drone flight path. Embodiments send a drone flight path from the overall flight plan to the drone with instructions to fix the problem.

Drone deployment for distributed asset maintenance and repair

Methods, computer program products, and systems are presented. The method computer program products, and systems can include, for instance: examining target application container configuration data to identify one or more target container base image referenced in the target application container configuration: subjecting script data associated to the one or more target container base image to text based processing for evaluation of security risk associated to the one or more container base image, the script data obtained from at least one candidate hosting computing environment; and selecting a hosting computing environment from the at least one computing environment for hosting the target application container, the selecting in dependence on the text based processing.

Software application container hosting

An approach is provided in which the approach calculates at least one weighting factor based on a word frequency analysis of an unlabeled document against a set of word frequencies corresponding to a set of labeled documents. The approach computes an a posteriori classification probability of the unlabeled document based on the at least one weighting factor, and creates an inferred classifier based on the a posteriori classification probability. The approach classifies the unlabeled classifier using the inferred classifier.

Infer text classifiers for large text collections

A method for predicting a time to a failure condition of an asset receives image pairs of a healthy condition asset and historical image pairs of the asset from inspections and at a failure condition of the asset. A first model is trained to rebuild input images to resemble a healthy condition asset. Similarity coefficients are generated for the historical image pairs of a regular and infrared image by use of the rebuilding of images by the first model as a similarity base and historical image pairs include a timestamp of image capture. A second model is trained to predict a time to an asset failure condition based on timestamps of image capture, timestamps of an asset failure condition, and similarity coefficients of the historical images. Responsive to receipt of a real-time image pair, the method computes a predicted time to a failure condition of the asset.

A method and a system for automating security updates in a multi-tiered architecture of a system environment. The method includes receiving a software update note relating to a multi-tiered architecture system environment and determining a patch in the software update note is initially compatible with the system environment. The method also includes predicting, by a patch acceptance machine learning model, the patch is patchable within the system environment and predicting, by an installation machine learning model, an installation patch time for the patch on the system environment. The method also includes installing the patch at the installation patch time on the system environment, and validating the patch installation using a validation machine learning model.

Analysis and implementation of security updates

Weather data, asset variable values, and failure data may be received. A correlation factor may be generated by comparing the weather data and the asset variable values. One or more weather variables may be selected based on the correlation factor. A predicted asset variable value may be determined based on the weather data and the selected weather variables. The weather data and the predicted asset variable values may be compared to predict a failure probability for a first asset segment.

Risk failure prediction for line assets

A method for asset maintenance includes determining a location of an asset, receiving meteorological data corresponding to the location, determining forecasted meteorological conditions for the asset from the meteorological data, determining a forecasted probability of degradation or failure of the asset based, at least in part, on the forecasted meteorological conditions, and dispatching asset maintenance services for the asset responsive to determining that the forecasted probability of degradation or failure of the asset is above a selected threshold. The method may also include collecting sensor data corresponding to the asset, and determining the forecasted probability of degradation or failure of the asset based, at least in part, on the forecasted meteorological conditions and the sensor data. A corresponding apparatus, computer program product, and computer system are also disclosed herein.

Predictive asset maintenance

Described are techniques for identifying anomalies in log files. The techniques including a method encompassing mapping a plurality of log entries into a bag-of-words matrix and determining respective cosine similarities between consecutive log entries in the bag-of-words matrix. The method further includes converting respective cosine similarities to respective states and determining respective state transitions between consecutive log entries. The method further includes generating a stochastic matrix based on the respective state transitions and identifying an anomalous log entry based on an anomalous state transition. The anomalous state transition has a probability below a probability threshold, and the probability is based on the stochastic matrix.

Anomaly identification in log files

Devices and methods for classification of low relevance records in a database are disclosed. A method includes: in response to a request to delete a selected database record, generating a vector representation of the selected record, deleting the selected record in the database, and storing the vector representation of the deleted selected record; in response to the storing the vector representation of the deleted selected record, determining a cluster from which the vector representation has a shortest determined distance, among a plurality of clusters into which a plurality of vector representations of deleted records is partitioned; determining a distance between a record in the database and a nearest cluster among the plurality of clusters into which the plurality of vector representations of deleted records is partitioned; and in response to the record being within a predetermined distance of the nearest cluster, determining that the record is a deletion candidate record.

System and method for classification of low relevance records in a database using instance-based classifiers and machine learning

A computer system for configuring an Integrated Development Environment display is provided. The computer system receives a list of system artifacts that is categorized by at least one artifact type and indexed by affected source code elements. In response to a user beginning an IDE session, the computer system assigning an impact relevance threshold to the user and, in response to the user selecting a source code element, the computer system identifies artifacts from the list of artifacts that affect the selected source code element. The computer system, in accordance with impact values assigned to the artifact types for the source code element, selects from the identified artifacts, a set of display artifacts having assigned impact values that meet or exceed the impact relevance threshold assigned to the user. The computer system the set of display artifacts on an IDE display element.

Computer enhanced and automatic computer-generated integrated development environment reconfiguration

A linear asset processing and managing system and method may include receiving a plurality of metrics associated with a linear asset segment from an asset management system over a network, in response to a request by the computing system to the asset management system, calculating an intermediate point on the geospatial context using a geometric analytic tool that analyzes the plurality of metrics received from the asset management system, determining a geospatial length of the linear asset segment, using the intermediate point and at least one metric of the plurality of metrics, and displaying the linear asset segment of the linear asset on the geospatial context.

Visualizing linear assets using client-side processing

Detecting motion-sensor data anomalies by registering an event associated with received motion-detector sensor data at a current timestamp, calculating, a timestamp delta between a previous timestamp and the current timestamp, and counting transitions between a set of timestamp deltas using a finite state machine. The anomaly detection also includes using a Markov chain transition matrix, generated from the finite state machine, to determine that, a probability of transitioning from a current state to a next state associated with the event, is less than a predetermined value, and registering an anomaly associated with the sensor and the event.

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São Carlos, Brazil

Thiago Bianchi