Abstract: Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises analyzing a performed query by identifying a plurality of indicative markers based on a pre-stored classification database associated with the performed query; generating a plurality of facets based on the analysis of the performed query; selecting at least two facets within the generated plurality of facets by determining a quantitative similarity value between each respective facet and the plurality of identified indicative markers associated with the performed query; dynamically ranking the selected facets by prioritizing the selected facets based on a calculated overall score associated with assigned weighted values for each selected facet in the generated plurality of facets using a supervised machine learning algorithm; and displaying the dynamically ranked facets within a user interface of a computing device associated with a user.

Abstract: An embodiment includes creating an action item record corresponding to an action item of an action plan record that is responsive to a service request. The action item record comprises a service requirement of the action item. The embodiment executes a querying process that searches vendor records for candidate vendors associated with the service requirement and returns a set of candidate vendors. The embodiment updates the action item record with the set of candidate vendors and determines an optimal vendor team based at least in part on reputation data and cost data associated with each of the candidate vendors. The embodiment updates the action plan record to include the optimal vendor team, which triggers creation of a vendor team dispatch request.

Abstract: Systems, devices, computer-implemented methods, and/or computer program products that facilitate software vulnerability analysis using relationship data extracted from disparate package-related sources. In one example, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise a knowledge induction component and a vulnerability component. The knowledge induction component can populate a package ontology for a range of packages with relationship data extracted from a plurality of disparate package-related sources. The vulnerability component can identify an implicit vulnerability impacting the range of packages using the package ontology and a vulnerability record regarding an explicit vulnerability for a package within the range of packages.

Abstract: An example operation may include one or more receiving notifications from one or more monitoring agents, each notification comprising a monitoring agent identifier, one or more configuration changes, and a timestamp corresponding to each configuration change, identifying incremental configuration changes that may require a disaster recovery retest, requesting a partial disaster recovery retest comprising the incremental configuration changes, the partial disaster recovery retest providing test coverage for a subset of a full disaster recovery test plan, and providing a request to a blockchain network to store information for the received notifications to a shared ledger of the blockchain network.

Abstract: Systems, devices, computer-implemented methods, and/or computer program products that facilitate software vulnerability analysis using relationship data extracted from disparate package-related sources. In one example, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise a knowledge induction component and a vulnerability component. The knowledge induction component can populate a package ontology for a range of packages with relationship data extracted from a plurality of disparate package-related sources. The vulnerability component can identify an implicit vulnerability impacting the range of packages using the package ontology and a vulnerability record regarding an explicit vulnerability for a package within the range of packages.

Abstract: Embodiments of the present invention provide a computer system, a computer program product, and a method that comprises analyzing a performed query by identifying a plurality of indicative markers based on a pre-stored classification database associated with the performed query; generating a plurality of facets based on the analysis of the performed query; selecting at least two facets within the generated plurality of facets by determining a quantitative similarity value between each respective facet and the plurality of identified indicative markers associated with the performed query; dynamically ranking the selected facets by prioritizing the selected facets based on a calculated overall score associated with assigned weighted values for each selected facet in the generated plurality of facets using a supervised machine learning algorithm; and displaying the dynamically ranked facets within a user interface of a computing device associated with a user.

Abstract: A base query having a plurality of base query terms is obtained. A plurality of problem log files are accessed. Words, contained in a corpus vocabulary, are extracted from the plurality of problem log files. Based on the words extracted from the plurality of problem log files, a first expanded query is generated from the base query. The corpus is queried, via a query engine and a corpus index, with a second expanded query related to the first expanded query.

Abstract: In an approach to content driven predictive auto completion of IT queries, an input phrase for an inquiry is received, where the input phrase is a sequence of words. Next words for the input phrase are predicted, where the prediction is based on a deep neural network model that has been trained with a corpus of documents for a specific domain. The next words are appended to the input phrase to create one or more predicted phrases. The predicted phrases are sorted, where the predicted phrases are sorted based on a similarity computation between the predicted phrases and the corpus of documents for the specific domain.

Abstract: Aspects of the invention include receiving, using a processor, a plurality of values of a performance indicator. A statistical analysis of the plurality of values of the performance indicator is performed, using the processor, to detect an anomaly pattern in the plurality of values of the performance indicator. A warning message about the detected anomaly pattern is sent to an alert recipient that is selected by a machine learning model trained to identify alert recipients based at least in part on detected anomaly patterns. Feedback about the warning message is received from the alert recipient. The feedback includes an interest of the alert recipient in receiving warning messages about the detected anomaly pattern. The machine learning model is updated based at least in part on the feedback.

Abstract: A method, computer program product, and system for upgrading an IT system are provided. The method comprises: determining a plurality of existing components of the IT system; determining at least one user component based on a user requirement; building a structural topology of the IT system in accordance with the plurality of existing components and the at least one user component, the structural topology comprising the plurality of existing components, at least one connection among the plurality of existing components, and the at least one user component with its conditional connection, the conditional connection comprising the dependency of the at least one user component; and providing at least one upgrade recommendation for the IT system in accordance with the structural topology.

Abstract: An example operation may include one or more of receiving, by a disaster recovery orchestrator, configuration changes independent from a disaster recovery test schedule, from one or more monitoring agents at each of an information technology system, analyzing incremental configuration changes since a previous disaster recovery test for potential to limit a need for unnecessary disaster recovery retest, determining component level changes at each information technology system that impact disaster recovery test readiness, initiating a partial disaster recovery retest without regard to periodic disaster recovery test schedules, in response to incremental configuration changes that are at the information technology system component-level, and invoking a blockchain service to generate blockchain transactions, the blockchain transactions committing configuration changes, disaster recovery testing actions, and disaster recovery testing results to a shared ledger of a blockchain network.

Abstract: An example operation may include one or more of receiving, by a disaster recovery orchestrator, configuration changes independent from a disaster recovery test schedule, from one or more monitoring agents at each of an information technology system, analyzing incremental configuration changes since a previous disaster recovery test for potential to limit a need for unnecessary disaster recovery retest, determining component level changes at each information technology system that impact disaster recovery test readiness, initiating a partial disaster recovery retest without regard to periodic disaster recovery test schedules, in response to incremental configuration changes that are at the information technology system component-level, and invoking a blockchain service to generate blockchain transactions, the blockchain transactions committing configuration changes, disaster recovery testing actions, and disaster recovery testing results to a shared ledger of a blockchain network.

Abstract: An example operation may include one or more receiving notifications from one or more monitoring agents, each notification comprising a monitoring agent identifier, one or more configuration changes, and a timestamp corresponding to each configuration change, identifying incremental configuration changes that may require a disaster recovery retest, requesting a partial disaster recovery retest comprising the incremental configuration changes, the partial disaster recovery retest providing test coverage for a subset of a full disaster recovery test plan, and providing a request to a blockchain network to store information for the received notifications to a shared ledger of the blockchain network.

Abstract: A method, computer program product, and system for upgrading an IT system are provided. The method comprises: determining a plurality of existing components of the IT system; determining at least one user component based on a user requirement; building a structural topology of the IT system in accordance with the plurality of existing components and the at least one user component, the structural topology comprising the plurality of existing components, at least one connection among the plurality of existing components, and the at least one user component with its conditional connection, the conditional connection comprising the dependency of the at least one user component; and providing at least one upgrade recommendation for the IT system in accordance with the structural topology.

Abstract: Automatic teller machine network management control system executes a deterministic optimization process based on a deterministic forecasted withdrawal demand to determine a cash refill amount, and executes a stochastic simulation process to determine an overall automatic teller machine network management cost based on a stochastic forecasted withdrawal demand and the cash refill amount. The executing of the deterministic optimization process and the executing of the stochastic simulation process are iterated based on updated scaling parameter from the stochastic simulation process and the cash refill amount determined by the deterministic optimization process at current iteration until a termination threshold is met.

Abstract: A base query having a plurality of base query terms is obtained. A plurality of problem log files are accessed. Words, contained in a corpus vocabulary, are extracted from the plurality of problem log files. Based on the words extracted from the plurality of problem log files, a first expanded query is generated from the base query. The corpus is queried, via a query engine and a corpus index, with a second expanded query related to the first expanded query.

Abstract: A base query having a plurality of base query terms is obtained. A plurality of problem log files are accessed. Words, contained in a corpus vocabulary, are extracted from the plurality of problem log files. Based on the words extracted from the plurality of problem log files, a first expanded query is generated from the base query. The corpus is queried, via a query engine and a corpus index, with a second expanded query related to the first expanded query.

Abstract: A mechanism is provided for identifying patterns of a set of software applications instances from their documents. The computer-implemented method begins with constructing different attribute vector types using a knowledge ontology. The knowledge ontology captures semantics based on keywords associated with resource attributes derived from one or more documents related to at least a portion of these software application instances. A knowledge base is built from the attribute vector types and the documents of these application instances. These are merged into the knowledge base with the knowledge base previously built from previous software application instances. Analytics are performed on the knowledge base to identify at least one of common patterns of deployments, configurations, or other attribute vector types, or a combination thereof.

Abstract: Aspects of the invention include receiving, using a processor, a plurality of values of a performance indicator. A statistical analysis of the plurality of values of the performance indicator is performed, using the processor, to detect an anomaly pattern in the plurality of values of the performance indicator. A warning message about the detected anomaly pattern is sent to an alert recipient that is selected by a machine learning model trained to identify alert recipients based at least in part on detected anomaly patterns. Feedback about the warning message is received from the alert recipient. The feedback includes an interest of the alert recipient in receiving warning messages about the detected anomaly pattern. The machine learning model is updated based at least in part on the feedback.

Abstract: Methods, systems, and computer program products for applying entity search techniques to expedite entitlement resolution in support services are provided herein. A computer-implemented method includes automatically processing multiple distributed data sources to identify data sources to be used in entitlement queries, identify data elements within each of the data sources, and implement search-enhancing features across the data sources; automatically searching the collection, in response to an entitlement query failure due to incorrect and/or missing data object values in a set of data object values input for the entitlement query, to identify alternate sets of values; applying a scoring algorithm to each of the alternate sets of values to generate a score that quantifies an amount of matching between each of the alternate sets and the set of data object values input in connection with the entitlement query; and outputting the alternate sets of values having the highest scores.