International Business Machines Corporation

Frank Bagehorn

Jinho Hwang

Larisa Shwartz

Michael Elton Nidd

Srinivasan Parthasarathy

Qing Wang

Ota Sandr

Altynbek Orumbayev

Jakub Krchák

Michal Mýlek

Daniel Nikolaus Bauer

Thomas A Jobson, Jr

Tomás Ondrej

Adeel Qureshi

Rafal Bigaj

Amol Bhaskar Mahamuni

Marcos Vinicius L Paraiso

Vidhya Shankar Venkatesan

Dorothea W Wiesmann Rothuizen

Michal Mylek

Robert Filepp

Tengfei Ma

Randall M George

Tomáš Ondrej

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

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

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

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

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 linking operational data with issues, a new event is received. The new event is associated to a story, where the story is related to an identified problem within the system, and further where the new event is associated with the story using machine learning techniques. The story is associated to related change requests based on a similarity between the story and related change requests, where the similarity between the story and the related change requests is associated using the machine learning techniques. A cost is calculated for the story. Responsive to associating the new event with a specific change request, the priority of the specific change request is updated based on the cost for the story.

Linking operational events with system changes

An approach to recommending corrective action to computing system event errors. The approach may include generating a textual description of an event error. The approach may include transforming the textual description into feature vectors with a domain-specific word embedding module. The approach may also include generating a recommendation to correct the event error based on an analysis of the feature vectors. Additionally, the recommendation may be presented for verification.

Computing system event error corrective action recommendation

A computer implemented method for identifying an application topology includes identifying a sandbox environment corresponding to an application of interest, analyzing the sandbox environment to identify a set of communication links between services within the sandbox environment indicating a first topology, identifying a production system corresponding to the application of interest, querying the production system to identify a set of structural dependencies indicating a second topology, and creating a complete topology of the cloud application by combining the first topology and the second topology. A computer program product and computer system for identifying an application topology are additionally disclosed herein.

Application topology discovery

An approach is provided for aggregating business data. Based on received columns, attributes, and keys of a dataset, the dataset is registered in a central hub of datasets which include data that is independently and locally maintained. Other datasets in the central hub that include columns, attributes, or keys that match the received columns, attributes, or keys, respectively, are determined and displayed. Responsive to receiving a user selection of one or more of the displayed datasets, cross-links between the dataset and each of the one or more selected datasets are generated. Based on a modification of a selected one of the cross-links and a second user interacting with the central hub, the modification and other cross-link(s) are displayed.

User driven business data aggregation and cross mapping framework

An approach is provided for aggregating business data. Based on received columns, attributes, and keys of a dataset, the dataset is registered in a central hub of datasets which include data that is independently and locally maintained. Other datasets in the central hub that include columns, attributes, or keys that match the received columns, attributes, or keys, respectively, are determined and displayed. Responsive to receiving a user selection of one or more of the displayed datasets, cross-links between the dataset and each of the one or more selected datasets are generated. Based on a modification of a selected one of the cross-links and a second user interacting with the central hub, the modification and other cross-link(s) are displayed. Based on the second user's selection of the modification or one of the other cross-link(s), an analysis of data in the dataset is performed.

An approach is provided for aggregating business data. Based on received columns, attributes, and keys of a dataset, the dataset is registered in a central hub of datasets which include data that is independently and locally maintained. Initial or updated data is loaded into the dataset in the central hub. Other datasets in the central hub that include columns, attributes, or keys that match the received columns, attributes, or keys, respectively, are determined and displayed. In response to receiving a user selection of one or more of the displayed datasets, cross-links between the dataset and each of the one or more selected datasets are automatically generated. The cross-links are based on the columns, attributes, or keys of the displayed datasets that match the received columns, attributes or keys. The cross-links provide an aggregation of business data in the dataset and the one or more selected datasets.

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Dottikon, Switzerland

Frank Bagehorn