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.

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: Discussed herein are techniques for migrating an application from a source cloud environment (SCE) to a target cloud environment (TCE). Responsive to a request received by an application migration service (AMS) to migrate an application executed in a first compute instance in the SCE to a second compute instance in the TCE, the AMS authenticates credentials of a user with respect to the SCE. Upon the credentials being successfully authenticated, the AMS generates a public key and a private key. The public key is transmitted to a service manager that injects the public key in the application executed in the first compute instance and the private key is assigned to a source agent. The source agent obtains one or more artifacts and configuration information that enable execution of the application based on the private key, which are installed by a target agent in the second compute instance in the TCE.

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: A system may monitor transaction data pertaining to a plurality of transaction types received by a network order fulfillment system. The system may classify the transaction data into a plurality of alarm types based on pre-defined impact of an alarm type to a given transaction type. The system may analyze a plurality of performance parameters influencing a performance of the network order fulfillment system, and identify a performance parameter exhibiting an anomaly based on historical data, a current status of the plurality of the performance parameters and a predefined prediction model. The system may ascertain whether the identified performance parameter negatively impacts the performance of the network order fulfillment system, based on evaluation rules. The system may proactively implement a remediation action to remediate a potential fault caused by the identified performance parameter when the identified performance parameter negatively impacts the performance of the network order fulfillment system.

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 embodiment provides a method, including: obtaining a plurality of previously submitted application documents, wherein each of the previously submitted application documents comprises information provided by a user who initiated a given previously submitted application document; clustering the plurality of previously submitted application documents into clusters of application documents based upon topics of the previously submitted application documents; selecting a representative application document; identifying entities contained within a given representative application document, wherein each of the entities corresponds to information to be entered into a new application document created from the given representative application document; and engaging in a dialogue with a user to create the new application document utilizing a similar representative application document to request information from the user, wherein the similar representative application document comprises a representative application d

Abstract: Methods, computer program products, and/or systems are provided that perform the following operations: in an information technology (IT) management system, grouping one or more ongoing service failure events into a service failure record; identifying a representative event for the service failure record; identifying one or more conversations that relate to the one or more ongoing service events; computing, using a similarity algorithm, feature similarity scores for respective conversations of the one or more conversations based, at least in part, on the features associated with the representative event and features associated with the respective conversations; linking a subset of the one or more conversations to the one or more ongoing service events in the service failure record based, at least in part, on the computed feature similarity scores; and providing the service failure record to a collaboration platform utilized in addressing the one or more ongoing service events.

Abstract: A method, system, and non-transitory processor-readable medium for optimizing lifecycle rule computer processing including: selecting a set of lifecycle rules to apply to interactions, finding a set of interactions satisfying criteria associated with the lifecycle rule; calculating an associated rule occupancy score based on the number of executed interactions associated with the rule, and executing the rule based on the rule occupancy score associated with the rule.

Abstract: A computerized-method for determining a storage class of a media file is provided herein. The computerized-method includes: (i) aggregating data from the plurality of data sources; (ii) operating a pre-built machine learning File Access Frequency Score (FAFS) model to calculate an accessibility score for a media file; (iii) sending the media file to archive storage when the accessibility score is below a preconfigured threshold; (iv) calculating Predicted Active Lifetime Span (PALS) score of the media file, when the accessibility score is above the preconfigured threshold; (v) repeating the calculating of the PALS score of the media file every preconfigured period of time, when the media file is in active storage, until the calculated PALS score of the media file is lower than or equals zero; and (vi) sending the media file to the archive storage.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate proactive operational risk assessment of a proposed change in a computing environment are provided. According to an embodiment, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise an extraction component that identifies change events in historic operational data that induced one or more incidents in a computing environment. The computer executable components further comprise an assessment component that employs a model to assign a change risk assessment score to a defined change in the computing environment based on the change events.

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: A computerized-method for gauging agent's self-assessment effectiveness, is provided herein. The computerized-method includes for each interaction (i) operating a Self-assessment Consolidation module to calculate a confidence-interval for each data-point of one or more preconfigured data-points, and (ii) operating a Self-assessment Divergence Determinant (SDD) module. The operating of the SDD includes: retrieving one or more data-points of the interaction; for each data-point retrieving the confidence interval; setting a divergence-indicator as zero, when the data point is within the confidence-interval; setting the divergence-indicator as a subtraction of the data point from the calculated lower-bound, when the data-point is lower than the lower-bound of the confidence-interval; and setting the divergence-indicator as a subtraction of the calculated upper-bound from the data-point, when the data-point is greater than the upper-bound of the confidence-interval.

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: 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: A system may monitor transaction data pertaining to a plurality of transaction types received by a network order fulfillment system. The system may classify the transaction data into a plurality of alarm types based on pre-defined impact of an alarm type to a given transaction type. The system may analyze a plurality of performance parameters influencing a performance of the network order fulfillment system, and identify a performance parameter exhibiting an anomaly based on historical data, a current status of the plurality of the performance parameters and a predefined prediction model. The system may ascertain whether the identified performance parameter negatively impacts the performance of the network order fulfillment system, based on evaluation rules. The system may proactively implement a remediation action to remediate a potential fault caused by the identified performance parameter when the identified performance parameter negatively impacts the performance of the network order fulfillment system.