公开(公告)号：US11664130B2

公开(公告)日：2023-05-30

申请号：US17009043

申请日：2020-09-01

Applicant: International Business Machines Corporation

Inventor： Girmaw Abebe Tadesse ,  Chen Lin ,  Roxana Monge Nunez ,  Maja Vukovic ,  Komminist Weldemariam

IPC: G16H50/80 ,  G16H50/50 ,  G16H50/70 ,  G16H40/20 ,  G06Q30/0204 ,  G16H50/30 ,  G06N20/00 ,  H04W4/12 ,  G09B19/00 ,  G16H50/20 ,  H04W4/021 ,  G16H10/60 ,  H04W4/029

CPC classification number: G16H50/80 ,  G06N20/00 ,  G06Q30/0205 ,  G09B19/00 ,  G16H40/20 ,  G16H50/20 ,  G16H50/30 ,  G16H50/50 ,  G16H50/70 ,  H04W4/12 ,  G16H10/60 ,  H04W4/021 ,  H04W4/029

Abstract: 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.

公开(公告)号：US20220083451A1

公开(公告)日：2022-03-17

申请号：US17019480

申请日：2020-09-14

Applicant: International Business Machines Corporation

Inventor： Chen Lin ,  Anup KALIA ,  Jin Xiao ,  John Rofrano ,  Saurabh Sinha ,  Raghav Batta ,  Maja Vukovic

IPC: G06F11/36 ,  G06F9/22 ,  G06F16/28

Abstract: A method, system, and computer program product for decomposing monolithic applications to form microservices are provided. The method identifies a set of classes within a monolithic application. A set of horizontal clusters are generated by performing horizontal clustering to the set of classes to decompose the classes based on a first functionality type. The method generates a set of vertical clusters by performing vertical clustering to the set of classes to decompose the classes based on a second functionality type. A subset of classes occurring in a common horizontal cluster and vertical cluster are identified as a functional unit. The method merges one or more functional units to form a microservice.

公开(公告)号：US20220035732A1

公开(公告)日：2022-02-03

申请号：US17500299

申请日：2021-10-13

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Jin Xiao ,  Anup Kalia ,  Chen Lin ,  Raghav Batta ,  Saurabh Sinha ,  John Rofrano ,  Maja Vukovic

IPC: G06F11/36 ,  G06F11/32

Abstract: 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.

公开(公告)号：US20220004428A1

公开(公告)日：2022-01-06

申请号：US16919178

申请日：2020-07-02

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Hongtan Sun ,  John Rofrano ,  Maja Vukovic ,  Chen Lin

IPC: G06F9/48 ,  G06N5/02 ,  G06N20/00 ,  G06F9/54

Abstract: An approach to optimized migration of user assets to the cloud using artificial intelligence is presented. This approach may 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.

公开(公告)号：US11176027B1

公开(公告)日：2021-11-16

申请号：US16855565

申请日：2020-04-22

Applicant: International Business Machines Corporation

Inventor： Jin Xiao ,  Anup Kalia ,  Chen Lin ,  Raghav Batta ,  Saurabh Sinha ,  John Rofrano ,  Maja Vukovic

IPC: G06F11/36 ,  G06F11/32

Abstract: 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.

公开(公告)号：US10803108B2

公开(公告)日：2020-10-13

申请号：US15848589

申请日：2017-12-20

Applicant: International Business Machines Corporation

Inventor： Nikolaos Anerousis ,  Anup Kalia ,  Chen Lin ,  Maja Vukovic ,  Jin Xiao

IPC: G06F16/36 ,  G06K9/62 ,  G06F16/25 ,  G06F16/28 ,  G06F16/22 ,  G06F16/35 ,  G06N3/08 ,  G06N3/04 ,  G06F16/31 ,  G06N5/02 ,  G06F40/30 ,  G06N20/10 ,  G06N7/00

Abstract: Techniques for generating domain and client-specific application program interface recommendations are provided. In one example, a computer-implemented method comprises modifying, by a device operatively coupled to a processor, a description of a client application program interface by removing text data associated with the description of the client application program interface, resulting in a modified description of the client application program interface. The computer-implemented method can further comprise analyzing, by the device, a performance associated with the client application program interface to generate an ontology based on a semantic similarity between the modified description of the client application program interface and one or more previous descriptions of one or more previous client application program interfaces.

公开(公告)号：US20190188319A1

公开(公告)日：2019-06-20

申请号：US15848589

申请日：2017-12-20

Applicant: International Business Machines Corporation

Inventor： Nikolaos Anerousis ,  Anup Kalia ,  Chen Lin ,  Maja Vukovic ,  Jin Xiao

IPC: G06F17/30 ,  G06F17/27 ,  G06K9/62

Abstract: Techniques for generating domain and client-specific application program interface recommendations are provided. In one example, a computer-implemented method comprises modifying, by a device operatively coupled to a processor, a description of a client application program interface by removing text data associated with the description of the client application program interface, resulting in a modified description of the client application program interface. The computer-implemented method can further comprise analyzing, by the device, a performance associated with the client application program interface to generate an ontology based on a semantic similarity between the modified description of the client application program interface and one or more previous descriptions of one or more previous client application program interfaces.

公开(公告)号：US11940904B2

公开(公告)日：2024-03-26

申请号：US18299846

申请日：2023-04-13

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Jin Xiao ,  Anup Kalia ,  Chen Lin ,  Raghav Batta ,  Saurabh Sinha ,  John Rofrano ,  Maja Vukovic

IPC: G06F11/36 ,  G06F11/32

CPC classification number: G06F11/3688 ,  G06F11/323 ,  G06F11/3636 ,  G06F11/3684 ,  G06F11/3692

Abstract: 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.

公开(公告)号：US20230251962A1

公开(公告)日：2023-08-10

申请号：US18299846

申请日：2023-04-13

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Jin Xiao ,  Anup Kalia ,  Chen Lin ,  Raghav Batta ,  Saurabh Sinha ,  John Rofrano ,  Maja Vukovic

IPC: G06F11/36 ,  G06F11/32

CPC classification number: G06F11/3688 ,  G06F11/3684 ,  G06F11/323 ,  G06F11/3636 ,  G06F11/3692

Abstract: 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.

公开(公告)号：US11501165B2

公开(公告)日：2022-11-15

申请号：US16809319

申请日：2020-03-04

Applicant: International Business Machines Corporation

Inventor： Chen Lin ,  Hongtan Sun ,  John Rofrano ,  Maja Vukovic

IPC: G06K9/62 ,  G06N3/08 ,  G06N20/00

Abstract: Embodiments relate to a system, program product, and method for training a contrastive neural network (CNN) in an active learning environment. A neural network is pre-trained with labeled data of a historical (first) dataset. The CNN is trained for a new (second) dataset by applying the new dataset and contrasting the new dataset against the historical dataset to extract novel patterns. Weights of a knowledge operator from the pre-trained neural network are borrowed. Features novel to the new dataset are learned, including updating weights of the knowledge operator. The borrowed knowledge operator weights are combined with the updated knowledge operator weights. The CNN is leveraged to predict one or more labels for the new dataset as output data.