Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: In an approach for automatically extracting product baseline information from a request for proposal document, a processor receives the document. A processor detects a table in the document. A processor identifies a table header on the table. The table header is associated with a name and an associated volume of the product. A processor extracts context based on the table header from the table. The context includes the name and the associated volume of the product. A processor maps the extracted context with the name of the product in the table to an associated name of the product based on a pre-defined product ontology.

Abstract: A method, a computer system, and a computer program product for managing a dataset of training samples, labeled by class, during training of a machine learning model is provided. Embodiments of the present invention may include training the model on a sequence of increasing-sized sets of the training samples and testing performance of the model after training with each set to obtain class-specific performance metrics corresponding to each set size. Embodiments of the present invention may include generating class-specific learning curves from the performance metrics for the plurality of classes. Embodiments of the present invention may include extrapolating the learning curves. Embodiments of the present invention may include optimizing a function of the predicted performance metrics to identify a set of augmentation actions to augment the dataset for further training of the model. Embodiments of the present invention may include providing an output indicative of the set of augmentation actions.

Abstract: Computer-implemented methods and systems are provided for identifying IT service compositions corresponding to subsets of a set R of IT service requirements. Such a method includes providing a data structure including, for a set S of IT services, a master graph having master nodes representing respective subsets of like services in S, interconnected by master edges each representing an integration-need between nodes interconnected by that edge. The method further comprises, for each service composition being a set of services, integrated by integration components and spanning all master nodes, in the composition subgraph, comparing the composite attributes of services and integration components in that composition with the requirements in R? to select at least one preferred service composition for R?, and outputting composition data defining each preferred service composition.

Abstract: A method, a computer system, and a computer program product for managing a dataset of training samples, labeled by class, during training of a machine learning model is provided. Embodiments of the present invention may include training the model on a sequence of increasing-sized sets of the training samples and testing performance of the model after training with each set to obtain class-specific performance metrics corresponding to each set size. Embodiments of the present invention may include generating class-specific learning curves from the performance metrics for the plurality of classes. Embodiments of the present invention may include extrapolating the learning curves. Embodiments of the present invention may include optimizing a function of the predicted performance metrics to identify a set of augmentation actions to augment the dataset for further training of the model. Embodiments of the present invention may include providing an output indicative of the set of augmentation actions.

Abstract: Computer-implemented methods and systems are provided for identifying IT service compositions corresponding to subsets of a set R of IT service requirements. Such a method includes providing a data structure including, for a set S of IT services, a master graph having master nodes representing respective subsets of like services in S, interconnected by master edges each representing an integration-need between nodes interconnected by that edge. The method further comprises, for each service composition being a set of services, integrated by integration components and spanning all master nodes, in the composition subgraph, comparing the composite attributes of services and integration components in that composition with the requirements in R? to select at least one preferred service composition for R?, and outputting composition data defining each preferred service composition.

Abstract: In an approach for automatically extracting product baseline information from a request for proposal document, a processor receives the document. A processor detects a table in the document. A processor identifies a table header on the table. The table header is associated with a name and an associated volume of the product. A processor extracts context based on the table header from the table. The context includes the name and the associated volume of the product. A processor maps the extracted context with the name of the product in the table to an associated name of the product based on a pre-defined product ontology.

Abstract: 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.

Abstract: Training a random forest model to relate settings of a network security device to undesirable behavior of the network security device is provided. A determination of a corresponding set of settings associated with each region of lowest incident probability is made using a random forest. The plurality of identified desired settings are presented as options for changing the network security device from the as-is settings to the identified desired settings. A choice is received from the plurality of options. The choice informs the random forest model. The random forest model ranks for a new problematic network security device the plurality of options for changing the new problematic network security device from as-is settings to desired settings by aggregating an identified cost of individual configuration changes, thereby identifying a most cost-effective setting for the network security device to achieve a desired output of the network security device.

Abstract: Training a random forest model to relate settings of a network security device to undesirable behavior of the network security device is provided. A determination of a corresponding set of settings associated with each region of lowest incident probability is made using a random forest. The plurality of identified desired settings are presented as options for changing the network security device from the as-is settings to the identified desired settings. A choice is received from the plurality of options. The choice informs the random forest model. The random forest model ranks for a new problematic network security device the plurality of options for changing the new problematic network security device from as-is settings to desired settings by aggregating an identified cost of individual configuration changes, thereby identifying a most cost-effective setting for the network security device to achieve a desired output of the network security device.

Abstract: In an approach to assisting database management, a computer generates one or more combinations of values of one or more database configuration parameters. The computer associates each of the one or more generated combinations of values with an incident probability. The computer defines relationships between the one or more generated combinations and the associated incident probabilities. The computer stores the defined relationships into an object representable as a multi-dimensional matrix, whose dimensions correspond to a plurality of database configuration parameters used to generate the combinations of values. The computer traverses the object to identify a path in the matrix. The computer returns the identified path for enabling subsequent interpretation thereof as a rule for passing from a first database configuration, corresponding to the first one of the one or more generated combinations, to a second database configuration, corresponding to the second one of the one or more generated combinations.

Abstract: A computer-implemented method of increasing reliability of an information technology environment comprising a plurality of hardware devices. Training data is received and a random forest is built from the training data using machine learning. A particular hardware device in the plurality of hardware devices is determined to be strange. Strange is defined as the particular hardware device having a proximity value lower than a predetermined threshold value for the random forest. A preventative action is determined to lower a risk of failure of the particular hardware device. The preventative action is reported. Reporting includes at least one of displaying a report on a display device, printing the report onto paper, and storing the report in a non-transitory computer recordable storage medium.

Abstract: In an approach to assisting database management, a computer generates one or more combinations of values of one or more database configuration parameters. The computer associates each of the one or more generated combinations of values with an incident probability. The computer defines relationships between the one or more generated combinations and the associated incident probabilities. The computer stores the defined relationships into an object representable as a multi-dimensional matrix, whose dimensions correspond to a plurality of database configuration parameters used to generate the combinations of values. The computer traverses the object to identify a path in the matrix. The computer returns the identified path for enabling subsequent interpretation thereof as a rule for passing from a first database configuration, corresponding to the first one of the one or more generated combinations, to a second database configuration, corresponding to the second one of the one or more generated combinations.

Abstract: In one embodiment, a computer-implemented method includes obtaining incident data related to a plurality of servers, including a first server. Configuration data is obtained for each of the plurality of servers. The configuration data includes information about a set of one or more configuration items of the first server. A predictive modeler is trained to predict incident characteristics, based at least in part on the incident data and the configuration data. A modification is selected to the set of configuration items of the first server. Predicted incident characteristics of the first server are simulated, by a computer processor, based on the selected modifications. It is recommended that the selected modifications be made to the first server if predetermined criteria are met by the simulation.

Abstract: In one embodiment, a computer-implemented method includes obtaining incident data related to a plurality of servers, including a first server. Configuration data is obtained for each of the plurality of servers. The configuration data includes information about a set of one or more configuration items of the first server. A predictive modeler is trained to predict incident characteristics, based at least in part on the incident data and the configuration data. A modification is selected to the set of configuration items of the first server. Predicted incident characteristics of the first server are simulated, by a computer processor, based on the selected modifications. It is recommended that the selected modifications be made to the first server if predetermined criteria are met by the simulation.

Abstract: An optical waveguide element having a cladding layer formed on a substrate and at least one waveguide core formed in the cladding layer for transmitting an optical signal. Two heater sections are provided for shifting a phase of optical signals. The two heater sections show different design. This helps controlling heater induced birefringence.

Abstract: A data storage device comprises a storage medium for storing data in the form of marks and at least one probe for scanning the storage medium. The storage medium may be comprised in a substrate. The probe comprises a cantilever that comprises terminals serving as electrical contacts an being during operation of the probe mechanically fixed to a probe-holding structure, which may be a common frame of the data storage device. A probe further comprises a supporting structure, to which the terminals are mechanically directly coupled or coupled via hinges and which extends away from the terminals. A tip with a nanoscale apex is provided. A beam structure comprises a heating resistor and is attached at ends to the supporting structure. The beam structure is thinned at least in a direction parallel to an axis of the tip compared to an area of the supporting structure abutting the beam structure.

Abstract: Provides surface treatment devices, surface scanning devices, methods of operating a surface treatment device and methods of operating a surface scanning device. An area within a medium comprises at least one sharpening location for sharpening a tip of a probe mechanically. The tip is conically shaped with a radius of an apex smaller than 100 nm. In the case of the surface treatment device the probe is designed for altering the surface of the medium. In the case of the surface scanning device the probe is designed for scanning the medium. The sharpening location is suited for sharpening the tip mechanically. For that purpose the probe and the medium are being moved relative to each other such that the tip is located in the sharpening location. Then the probe and/or the medium are moved relative to each other such, that the tip is mechanically sharpened.