Abstract: A method and system is provided for utilizing a causal dependence graph of events in a large enterprise-related system to determine a most frequently utilized corrective action for a set of actions that the enterprise requires. Typically, with large sets of data related to actions that an enterprise system performs, it is non-trivial to correlate a set of actions (or workflows) with a set of corrective actions.

Abstract: A method, computer system, and a computer program for data selection is provided. The present invention may include generating a first model associated with a dataset. The present invention may further include determining a first model performance level associated with the first model based on a plurality of dataset metric values of the dataset. The present invention may further include a plurality of data subsets of a dataset based on the first model performance level failing to exceed a performance threshold and calculating a plurality of subset metric values associated with the plurality of data subsets. The present invention may further include generating a second model associated with at least one data subset based on the plurality of subset metric values and determining an optimization associated with the first model based on a second model performance level associated with the second model exceeding the performance threshold.

Abstract: One or more computer processors classify each log line in a plurality of unlabeled log lines as an erroneous log line or a non-erroneous log line. The one or more computer processors templatize each classified erroneous log line and non-erroneous log line in the plurality of unlabeled log lines. The one or more computer processors cluster erroneous log templates into erroneous log template clusters and the non-erroneous log templates into non-erroneous log template clusters. The one or more computer processors eliminate the erroneous log template clusters and the non-erroneous log template clusters that exceed a frequency threshold. The one or more computer processors train a log anomaly model utilizing=remaining erroneous log template clusters and remaining non-erroneous log template clusters. The one or more computer processors identify a subsequent log line as anomalous or non-anomalous utilizing the trained log anomaly model.

Abstract: Discussed herein is a framework that facilitates access to services offered in a target cloud environment for resources deployed in a source cloud environment. The source cloud environment is different and independent with respect to the target cloud environment. A compute instance executed in a source cloud environment generates a request to use a service provided in the target cloud environment. The request is transmitted from the source cloud environment to the target cloud environment via an intercloud service gateway. The service is executed in the target cloud environment based on an access role that is associated with the compute instance.

Abstract: Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises identifying a plurality of data logs; generating a data model using analyzed time series data from the identified data logs; detecting anomalies within the generated data model; constructing a causal graph using the detected anomalies and retrieved domain knowledge; computing a severity value for the detected anomalies with the constructed causal graph; assigning the detected anomaly to a classification based on a function vector, wherein the computed severity value is a function vector; and automatically modifying a function of a computing device based on the function vector of the assigned, detected anomaly, wherein a modification addresses the detected anomaly located at a center of the constructed casual graph.

Abstract: A computer-implemented method gathers a set of previously filed applications, performs intent identification on the set, clusters applications in the set based on identified common intents, receives input from user and identifying intent of the input, matches the input to a cluster based on a common intent, and generates a new application based on the input using filed applications in a cluster having a common intent between the cluster and the input.

Abstract: The present disclosure relates to a framework that provides execution of serverless functions in a cloud environment based on occurrence of events/notifications from services in an entirely different cloud environment. A target agent obtains a notification from a source agent, where the target agent is deployed in a target cloud environment and the source agent is deployed in a source cloud environment that is different than the target cloud environment. The target agent determines a function that is to be invoked based on the notification. Upon successfully verifying whether the target agent is permitted to invoke the function that is deployed in a target customer tenancy of the target cloud environment, the target agent invokes the function in the target customer tenancy of the target cloud environment.

Abstract: Methods, systems, and computer program products for automatically detecting periods of normal activity by analyzing observability data in IT operations environments are provided herein. A computer-implemented method includes obtaining multiple types of data related to one or more artificial intelligence-related information technology operations; modelling at least a portion of the obtained data as time series data; automatically identifying, from the time series data, one or more time periods associated with one or more given levels of data activity; and performing one or more automated actions, in at least one artificial intelligence-related information technology operations environment, based at least in part on the data corresponding to the one or more identified time periods.

Abstract: Identifying an log anomaly resolution by generating a knowledge base linking each of a plurality of incidents with historical anomalous log lines, calculating a resolution specificity score for each knowledge base record, identifying a run-time anomalous log line using the knowledge base, predicting a category for the run-time anomalous log line, identifying resolutions according to the category, ranking the resolutions according to the resolution specificity scores, and recommending a resolution according to the ranking.

Abstract: A computerized-method for calculating an After-Call-Work (ACW) factor of an interaction in a contact center, by which a related recording may be filtered for evaluation is provided herein. The method includes an After-Call-Work (ACW) factor calculation module. The operating of the ACW factor calculation module includes: (i) receiving agent recording of the interaction. (ii) aggregating data fields associated with: (a) the interaction; and (b) the customer; (iii) retrieving ACW time of the interaction; (iv) forwarding the aggregated data fields to a machine learning model; (v) operating the machine learning model to calculate a predicted ACW time, based on the aggregated data fields; (vi) calculating an ACW factor based on the received time of ACW and the calculated predicted ACW time; and (vii) sending the calculated ACW factor to a platform by which the platform is preconfigured to distribute the interaction for evaluation, based on the ACW factor.

Abstract: One or more computer processors classify each log line in a plurality of unlabeled log lines as an erroneous log line or a non-erroneous log line; templatize each classified erroneous log line and non-erroneous log line in the plurality of unlabeled log lines; cluster erroneous log templates into erroneous log template clusters and non-erroneous log templates into non-erroneous log template clusters; identify one or more log lines as anomalous utilizing a plurality of factors including a log maturity, a number of encountered log template clusters, and a ratio of classified erroneous log lines to classified non-erroneous log lines; responsive to one or more identified anomalous log lines, validate the identified anomalous log lines utilizing a site reliability engineer and human-in-the-loop validation; train a log anomaly model utilizing one or more validated log lines; and identify a subsequent log line as anomalous utilizing the trained log anomaly model.

Abstract: One or more computer processors classify each log line in a plurality of unlabeled log lines as an erroneous log line or a non-erroneous log line. The one or more computer processors templatize each classified erroneous log line and non-erroneous log line in the plurality of unlabeled log lines. The one or more computer processors cluster erroneous log templates into erroneous log template clusters and the non-erroneous log templates into non-erroneous log template clusters. The one or more computer processors eliminate the erroneous log template clusters and the non-erroneous log template clusters that exceed a frequency threshold. The one or more computer processors train a log anomaly model utilizing=remaining erroneous log template clusters and remaining non-erroneous log template clusters. The one or more computer processors identify a subsequent log line as anomalous or non-anomalous utilizing the trained log anomaly model.

Abstract: 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.

Abstract: Embodiments of the present invention provide methods, computer program products, and systems. Embodiments of the present invention can in response to receiving information associated with an activity, identify requirements associated with performance of the activity. Embodiments of the present invention can then determine relevancy of each requirement of the identified requirements. Embodiments of the present invention can then define compliance mechanisms needed to verify compliance of the identified requirements that were determined relevant, and in response to detecting a violation, execute one or more remedial actions to satisfy an identified requirement that was determined relevant of the identified requirements.

Abstract: Methods, systems, and computer program products for data filtering with fuzzy attribute association are provided herein. A computer-implemented method includes obtaining one or more rules, specified by an expert, that define a partial ranking of a plurality of fuzzy pairings between (i) a plurality of item attributes for items in a data catalog and (ii) a plurality of user attributes related to said items; generating an interactive session with the expert to resolve one or more ambiguities in the one or more rules; and deriving a scoring function based at least in part on (i) the one or more rules and (ii) the resolved one or more ambiguities, wherein the scoring function generates a comparative score between any two items of said data catalog for a given one of the users associated with the plurality of attributes.

Abstract: 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.

Abstract: One or more systems, computer-implemented methods and/or computer program products to facilitate a process to transform original operational data into updated operational data. 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 transformation component that can transform original operational data of a first architecture into updated operational data employable at a second architectures, wherein the second architectures is an updated architectures relative to the first architecture. In one or more embodiments, the transformation component further can employ machine learning to match one or more data elements of the original operational data to one or more aspects of the second architecture.

Abstract: A new and innovative high-pressure priming valve is provided for use in high-pressure fluid systems that require a high level of fluid purity. The priming valve includes at least three ports, some of which are angled. The priming valve also includes a needle that variably blocks and unblocks a pathway to one of the ports between normal operation and a priming operation, respectively. The priming valve includes a sealing insert positioned below a stack of washers that maintain the needle's alignment in response to high fluid pressures exerted on the needle. The sealing insert helps prevent fluid from contacting the stack of washers, which helps prevent biological growth within the valve. The angled ports help facilitate priming valve drainage to further help prevent biological growth. By helping prevent biological growth, the sealing insert helps prevent fluid contamination and enables the priming valve to be utilized for high-purity fluid applications.

Abstract: An approach for generating actionable explanations of change request classifications may be presented. A model may generate features associated with a change request may be disclosed. The model may be trained with historical change requests that have been labeled risky or not risky. The change request may be classified as risky or not risky. Candidate historical change requests with the same classification as the change request and occupying similar feature space as the change request may be identified from a historical change request repository. One or more features which had the most significant impact on the classification may be identified. A candidate historical change request with at least one significant feature impacting classification may be identified.

Abstract: Methods, systems, and computer program products for generating dialog system workspaces are provided herein. A computer-implemented method includes obtaining (i) a set of policy documents and (ii) a set of initial questions; identifying at least one of the policy documents in the set of policy documents that is relevant to answering a given one of the initial questions in the set of initial questions; generating, based at least in part on an analysis of said identified policy document, (i) at least one follow-up question to said given initial question and (ii) two or more candidate answers to said at least one follow-up question; generating a dialog tree comprising at least (i) a parent node corresponding to the at least one follow-up question and (ii) child nodes corresponding to the two or more candidate answers; translating the dialog tree into a dialog workspace; and deploying the dialog workspace in an intelligent dialog system.