International Business Machines Corporation

Florida International University

St. John's University

Technion Research & Development Foundation Limited

Larisa Shwartz

Genady Grabarnik

Jinho Hwang

Lev Kozakov

Michael Elton Nidd

David Matthew Loewenstern

Frank Bagehorn

Qing Wang

Florian Pinel

Naga A Ayachitula

Christopher Ward

Melissa J Buco

Maheswaran Surendra

Srinivasan Parthasarathy

Altynbek Orumbayev

Ota Sandr

Jakub Krchák

Dimitri Kanevsky

Jonathan Lenchner

Yixin Diao

Amitkumar Manoharrao Paradkar

Alexander Faisman

Pooja Aggarwal

Raghav Batta

Maira Athanazio De Cerqueira Gatti

Ricardo Guimaraes Herrmann

Victoria Lee Guerra

Michal Mýlek

Maja Vukovic

Seema Nagar

Moon Jung Kim

Yurdaer Nezihi Doganata

Joel W Branch

Karin Murthy

Krishna Sastry Garimella

Tomás Ondrej

Yaoping Ruan

Shang Qing Guo

Ajay Gupta

Tao Li

Moon Ju Kim

Milton H Hernandez

James Sargent Lipscomb

Rafal Bigaj

Debanjan Saha

Cesar Augusto Gonzales

Chee Meng Low

Harshit Kumar

Amar Prakash Azad

Amol Bhaskar Mahamuni

Sinem Guven Kaya

Stefan Tai

Marcos Vinicius L Paraiso

Sinem Guven

Vidhya Shankar Venkatesan

Dorothea W Wiesmann Rothuizen

Bing Zhou

Yan Or

John Robert Davis

Zhen Liu

Anton Viktorovich Riabov

Irina Rish

Achille Belly Fokoue-Nkoutche

Kavitha Srinivas

Heiko Hary Ludwig

Adi Ionel Botea

Dikran S Meliksetian

Akihiro Kishimoto

Nagui Halim

Radu Marinescu

Mark Edward Podlaseck

Anand Ranganathan

Alberto Giammaria

Marco Luca Sbodio

Nikolaos Anerousis

Robert Filepp

Shu Tao

Gargi Banerjee Dasgupta

George E Stark

Yufang Hou

Rohit Madhukar Khandekar

Linh Hue Lam

Abhijit Bose

Pritam S Gundecha

Rajeev Puri

Edith Gail Schonberg

Laura Zaihua Luan

Robyn Lee Focazio

Tengfei Ma

Hiroshi Kajino

Rohan R Arora

Victor Rafael Herrero

Hongtan Sun

Marci Ramona Wojcik

Prateeti Mohapatra

Ioana Giurgiu

Saurabh Jha

Emi K Olsson

Jesus M Rios Aliaga

Randall M George

Mechanisms are provided that detect an anomaly in performance of a hybrid application based on a specification of required performance and collected passive monitoring data, and that generate a causal generative model based on relationships between hybrid application components and computing system architecture components extracted from the passive monitoring data. Root cause identification (RCI) logic is executed on the causal generative model to identify a set of candidate root causes of the detected anomaly. One or more probes are identified for active monitoring data collection targeting the identified set of candidate root causes, which are then executed to collect probe data. Reinforcement learning is performed of the RCI logic to update the RCI logic based on the probe data. The set of candidate root causes is updated based on the reinforcement learning.

Root cause identification in hybrid applications via probing

Several aspects are provided for dynamically updating an alert-management system that uses a master ruleset to match alerts in a data processing system with automata for handling the alerts. A method comprises training a machine learning model to correlate the alerts with the automata using a training dataset comprising alerts which were successfully handled by the automata. The machine learning model is then applied to correlate unmatched alerts with the automata, wherein the unmatched alerts were not matched to the automata by the master ruleset. The method further comprises analyzing operation of the machine learning model in relation to correlation of the unmatched alerts to define a new ruleset for matching the unmatched alerts with the automata and outputting the new ruleset for auditing of each rule in the new ruleset. In response to approval of an audited rule, the audited rule is added to the master ruleset.

Alert management in data processing systems

Localizing a faulty microservice in a microservice architecture is achieved by developing healthy execution sequence data for comparison to execution sequences during system failures. Oftentimes the faulty microservice does not emit a failure signal. Frequent sub-sequences arising from log template time series data during healthy execution facilitates localization of faulty services when there is no failure signal from the faulty service.

Fault localization in a distributed computing system

A method, a computer system, and a computer program product for a shiftleft topology construction is provided. Embodiments of the present invention may include collecting datasets. Embodiments of the present invention may include extracting topological entities from the datasets. Embodiments of the present invention may include correlating a plurality of data from the topological entities. Embodiments of the present invention may include mapping the topological entities. Embodiments of the present invention may include marking entry points for a plurality of subgraphs of the topological entities. Embodiments of the present invention may include constructing a topology graph.

Shiftleft topology construction and information augmentation using machine learning

A computer-implemented method and a computer system for identifying persistent anomalies for failure prediction. The computer system receives a time series data stream. The computer system received a predetermined number N and a predetermined number M which is a fraction of N. The computer system segments the time series data stream into N consecutive sliding windows. The computer system performs supervised persistent anomaly detection to determine whether anomalies across the N consecutive sliding windows are persistent, by using a binary classification model. The computer system performs unsupervised persistent anomaly detection to determine whether the anomalies across the N consecutive sliding windows are persistent. The computer system combines results of the supervised persistent anomaly detection and results of the unsupervised persistent anomaly detection to determine persistent anomalies.

Identifying persistent anomalies for failure prediction

Embodiments of the present invention provide methods, computer program products, and systems. Embodiments of the present invention can dynamically determine one or more endpoints to fulfill a user request. Embodiments of the present invention can select the dynamically determined one or more endpoints as the one or more endpoints that fulfill the user request. Embodiments of the present invention can execute the selected one or more endpoints to fulfill the user request.

Just in time assembly of transactions

Embodiments for intelligent application scenario testing and error detection by a processor. One or more modified application scenarios may be automatically generated from an initial application scenario having configuration data and a plurality of operations relating to an error. The one or more modified application scenarios are variations of the initial application. The one or more modified application scenarios may be executed to detect the existence or non-existence of the error in the one or more modified application scenarios.

Intelligent application scenario testing and error detection

A method, a computer system, and a computer program product for security risk analysis is provided. Embodiments of the present invention may include collecting operational data. Embodiments of the present invention may include building pipelines. Embodiments of the present invention may include localizing security issues using the operational data on an unsupervised model. Embodiments of the present invention may include constructing a semantic graph using shift-left data. Embodiments of the present invention may include constructing a mapping between the operational data and the shift-left data. Embodiments of the present invention may include clustering collected datasets. Embodiments of the present invention may include creating an active learning cycle using ground truth.

Shift-left security risk analysis

Systems, computer-implemented methods, and computer program products that can facilitate refinement of a predicted event based on explainability data are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can comprise an interpreter component that identifies a probable cause of a predicted event based on explainability data. The computer executable components can further comprise an enrichment component that executes a diagnostic analysis based on the probable cause.

Refinement of a predicted event based on explainability data

Techniques that facilitate feedback loop learning between artificial intelligence systems are provided. In one example, a system includes a monitoring component and a machine learning component. The monitoring component identifies a data pattern associated with data for an artificial intelligence system. The machine learning component compares the data pattern to historical data patterns for the artificial intelligence system to facilitate modification of at least a component of the artificial intelligence system and/or one or more dependent systems of the artificial intelligence system.

Feedback loop learning between artificial intelligence systems

Embodiments of the present invention provide computer-implemented methods, computer program products and systems. Embodiments of the present invention can receive position and location information. Embodiments of the present invention can generate a risk score for one or more maneuvers associated with a predicted trajectory of a vehicle. Embodiments of the present invention can generate a visual representation for each of the one or more maneuvers associated with the predicted trajectory of the vehicle based on the generated risk score associated with each maneuver. Embodiments of the present invention can integrate the generated visual representation into a user display.

Portable lane departure detection

A method, a computer system, and a computer program product for mapping operational records to a topology graph. Embodiments of the present invention may include generating an event frequent pattern using operational records. Embodiments of the present invention may include integrating topology-based event frequent patterns. Embodiments of the present invention may include mapping the operational records with an embedding engine. Embodiments of the present invention may include predicting incident events. Embodiments of the present invention may include receiving labeled patterns to the embedding engine for an active learning cycle.

Automatic mapping of records without configuration information

In an approach to risk prediction for bug-introducing changes, a computer retrieves one or more historic pull requests. A computer determines a unique file linking for each file included in the historic pull requests. A computer generates a file risk dataset. A computer performs chronological partitioning on the file risk dataset. A computer determines bug-introducing changes in the file risk dataset. A computer computes a collaborative file association between two or more of the files in the file risk dataset. A computer labels each of the files in the file risk dataset with an associated risk of introducing a bug. A computer generates a labelled file risk inducing ground truth dataset. A computer inputs the labelled file risk inducing ground truth dataset to a file risk prediction model. A computer extracts pull request features from the historic pull requests. A computer generates a pull request risk prediction model.

Pull request risk prediction for bug-introducing changes

A system and associated method may utilize a processor that receives recall product instance group (RPIG) information (RPIGI) identifying a set of instances of products having a product type identifier (PTI) that are a subject of a product recall. The processor receives, from an imaging device associated with a user, an image containing a product instance. The method further comprises applying a classifier and determining a PTI. Based on the image, secondary product information is also identified. These may be compared with the RPIGI to determine a product instance membership. Information related to the product instance membership in the RPIG may be displayed.

Targeted alerts for food product recalls

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Scarsdale, NY, United States of America

Larisa Shwartz