International Business Machines Corporation

Kyndryl, Inc.

Nec Corporation

Airbnb, Inc.

Kyndral, Inc.

Maja Vukovic

Clifford A Pickover

Jinho Hwang

James R Kozloski

Anup Kalia

Jin Xiao

Michael S Gordon

Valentina Salapura

Komminist S Weldemariam

John Joseph Rofrano

Itzhack Goldberg

Jim Alain Laredo

Constantin Mircea Adam

Muhammed Fatih Bulut

Thomas David Erickson

Milton H Hernandez

Kun Bai

Minkyong Kim

Sriram K Rajagopal

Nikolaos Anerousis

Roxana Monge Nunez

Brian L Peterson

Rhonda L Childress

Neil Sondhi

Saurabh Sinha

Frederick Y Wu

Rogerio Schmidt Feris

Sai Zeng

Maryam Ashoori

John Erik Wittern

Richard Jay Cohen

Ashish Kundu

Jie Ma

Larisa Shwartz

Yu Deng

Biplav Srivastava

Thomas E Chefalas

Zon-Yin Shae

Ya Bin Dang

Gopal Sarma Pingali

Robert Filepp

Aleksander A Slominski

Vinod Muthusamy

Rohit Madhukar Khandekar

Shikhar Kwatra

Hubertus Franke

John Alan Bivens

Jose Eduardo Moreira

Harigovind Venkatraj Ramasamy

Steven Alan Cordes

Hui Lei

Liangzhao Zeng

Nicholas C M Fuller

Michael Elton Nidd

Shripad Nadgowda

Naga A Ayachitula

Christopher Charles Young

Karin Murthy

Pritpal Arora

Richard Baxter Hull

Guan Zhang

Shivali Agarwal

Li Feng Zhang

Rick A Hamilton, Ii

Michael K Gschwind

James R Kraemer

Robert R Friedlander

Zhong Su

Ruchi Mahindru

Sara H Basson

Roman Vaculin

Sheng Hua Bao

Mahesh Viswanathan

Edgar Adolfo Zamora Duran

Yaoping Ruan

Geetika Tewari Lakshmanan

Soumitra Sarkar

Elizabeth Daly

Li Jun Mei

Vijay Krishnarao Naik

Kush Raj Varshney

Vugranam Chakravarthy Sreedhar

Mark Edward Podlaseck

Olympia Gluck

Honglei Guo

Joel W Branch

Oznur Alkan

Aleksandra Mojsilovic

Alaa Salah Youssef

Amit Dhurandhar

Shu Tao

Rachel Tzoref-Brill

Agueda Martinez Hernandez Magro

Senthil Kumar Kumarasamy Mani

Qi Hu

George E Stark

Santhosh Babu Kumaran

Eli Arbel

Kumar Bhaskaran

Zhili Guo

An approach to optimized migration of user assets to the cloud using artificial intelligence is presented. This approach may include user input and artificial intelligence trained with historical knowledge to generate rules. Migration models may be generated from the rules. A user may verify the migration models were successful. A task portfolio may be generated from the verified wave migration models. Runbook applications may be generated from the task portfolio and the migration may be executed using the runbooks.

Artificial intelligence optimized cloud migration

A system, computer program product, and method are presented for executing a validation test on at least a portion of a refactored application. The refactored application is resident within a partitioned computing architecture including a plurality of partitions and communication between the partitions uses one or more boundary methods. The refactored application is at least partially similar to a transformed monolithic application. The method includes automatically generating one or more test sequences for exercising one or more functional behaviors for each boundary method of the one or more boundary methods.

Validating inter-partition communication in microservice decomposition

Users of an endpoint remediation system can be assigned to different roles, from which they can request exceptions, approve exceptions, and/or enable remediation on endpoint devices. The compliance scanning and enforcing process can be automated, while allowing entities to request and/or approve certain exceptions. Therefore, security compliance for customers can be actively managed to provide visibility to the endpoint device compliance state at any time.

Testing and remediating endpoint device compliance controls

A method for guiding an emergency vehicle to an emergency site includes receiving an emergency dispatch message including a location of an emergency. Present location information is received for an emergency vehicle. A route between the received present location and the received location of the emergency is calculated using area map data. Navigation guidance is provided to the emergency vehicle based on the calculated route. The calculated route and the present location information for the emergency vehicle are transmitted to an unmanned aerial vehicle (UAV). The UAV is automatically piloted ahead of the emergency vehicle, along the calculated route, using the calculated route and present location transmitted thereto. A traffic alert is transmitted from the UAV to influence traffic flow ahead of the emergency vehicle.

Method for guiding an emergency vehicle using an unmanned aerial vehicle

Embodiments relate to monitoring an information technology (IT) environment having a plurality of domains through key performance indicator (KPI) data. In response to detection of a technical health problem, a first KPI related to the problem is identified. A root cause analysis is performed on the identified KPI generating a knowledge graph. A second KPI related to the first KPI is identified through the discovery of a correlation between the two identified KPIs. A diagnosis is generated for the technical health problem within the IT environment based on the discovered hidden correlation between the first KPI and second KPI. The generated diagnosis includes the root cause of the technical health issue.

Root cause identification and analysis

Systems, computer-implemented methods, and computer program products to facilitate generation of microservices from a monolithic application based on runtime traces 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 a model component that learns cluster assignments of classes in a monolithic application based on runtime traces of executed test cases. The computer executable components can further comprise a cluster component that employs the model component to generate clusters of the classes based on the cluster assignments to identify one or more microservices of the monolithic application.

Generation of microservices from a monolithic application based on runtime traces

Systems, computer-implemented methods, and computer program products to facilitate modernization of an application 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 a determination component that determines one or more relevant surrounding contexts for a raw entity. The computer executable components also can comprise a matching component that matches the one or more relevant surrounding contexts with one or more known surrounding contexts of one or more known entities. The computer executable components further can comprise a type identification component that identifies an entity type for the raw entity based on the matching of the one or more relevant surrounding contexts with the one or more known surrounding contexts.

Modernization of an application for related image generation

Techniques regarding discovering configuration information for one or more computer applications are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can comprise a configuration component that can discover configuration information associated with a containerized computer application. The configuration information can be characterized by a set of environment attributes extracted by querying a source code of the containerized computer application.

Configuration discovery of computer applications

Techniques are described for providing live first aid response guidance using a machine learning based cognitive aid planner. In one embodiment, a computer-implemented method is provided that comprises classifying, by a system operatively coupled to a processor, a type of an injury endured by a patient. The method further comprises, employing, by the system, one or more machine learning models to estimate a risk level associated with the injury based on the type of the injury and a current context of the patient.

Providing live first aid response guidance using a machine learning based cognitive aid planner

A technique relates to natural language automation to implement service requests. An intent of a service request is determined by accessing a knowledge base, the knowledge base being configured for dynamic discovery of dependencies related to configuration items, the configuration item being among the configuration items, the configuration items being associated with concepts. An intent application programming interface (API) database comprising a specification is accessed, the specification describing parameters of APIs and associations that the APIs have with the concepts of the knowledge base. Associated parameters of an API associated with the intent of the service request are determined based on the intent API database. The API is caused to be executed to accomplish the service request.

Performing natural language based reasoning and automation by transforming a configuration management database to a knowledge base

Techniques regarding providing artificial intelligence problem descriptions are provided. For example, one or more embodiments described herein can comprise a system, which can comprise a memory that can store computer executable components. The system can also comprise a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can include, at least: a query component that generates key performance indicators from a query, determines a subset of key performance indicators that individually have a performance below a threshold, and maps the subset of key performance indicators to operational metrics; a learning component that generates, using artificial intelligence, problem descriptions from one or more of the subset of key performance indicators or the operational metrics and transmits the problem descriptions to a database.

Artificial intelligence based problem descriptions

Systems, computer-implemented methods, and computer program products that can facilitate extraction of named entities from document data to support automation applications 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 entity extraction component that extracts, using a first machine learning process, a first data entity and a second data entity from document data indicative of a textual information. The computer executable components can further comprise a relation extraction component that determines, using a second machine learning process, a relation between the first data entity and the second data entity to generate a knowledge data graph used to execute an application associated with natural language processing for the document data.

Extraction of named entities from document data to support automation applications

A shift-left analysis system receives information regarding an application implemented by one or more microservices. The system determines a microservice performance metric based on a performance prediction model for each microservice of the application. The system outputs an application performance metric for the application based on the microservice performance metrics determined for the one or more microservices of application.

Estimating performance and required resources from shift-left analysis

Predicting infection risk by generating a first temporal graph of a first set of disease progression data, generating a second temporal graph of a second set of disease progression data, combining a first temporal graph node embedding and a second temporal graph node embedding, and generating a predicted infection risk according to the first temporal graph node embedding and the second temporal graph node embedding.

Predicting infection risk using heterogeneous temporal graphs

Growing community of inventors

New York, NY, United States of America

Maja Vukovic