Abstract: A question and answer pair is received from an external knowledge base. From the question, a set of intents is extracted. Whether the set of intents exceeds a match threshold with a subset of a plurality of intents within an internal knowledge base is determined. In response to determining a match threshold success, associating the question with the subset of intents within the plurality. A virtual assistant is trained to answer the question using the subset of intents.

Abstract: One or more computer processors responsive to a hypervisor data request, generate an encrypted set of hypervisor data indicating hypervisor hardware details using a plurality of respective asymmetric encryption keys to tag the generated set of encrypted hypervisor data with a timestamp at a time of encryption. The one or more computer processors decrypt the tagged set of encrypted hypervisor data utilizing a software asset manager and the plurality of respective asymmetric encryption keys determined valid based on a threshold time period and the tagged timestamp. The one or more computer processors validate the set of decrypted hypervisor data utilizing corresponding checksums to determine whether the set of encrypted hypervisor data was successfully decrypted. The one or more computer processors sanitize the validated hypervisor data based on a corresponding software license.

Abstract: An example operation may include one or more of detecting a web session within an open window of a web browser on a user device, monitoring for user activity on the user device with respect to the web browser, determining there has been a lack of user activity with respect to the web browser for a predetermined amount of time based on the monitoring of the user activity, and temporarily concealing content within the open window of the web browser in response to the lack of user activity.

Abstract: One or more computer processors install a trigger on a primary database. The one or more computer processors, responsive to the trigger activating and a data modification associated with the primary database, format the data modification into a universal format for a plurality of backup databases. The one or more computer processors rotate an active backup database from the plurality of backup databases based on a determined alternating backup period. The one or more computer processors synchronize in real-time the primary database with the active backup database.

Abstract: Containerized software discover and identification can include discovering a plurality of container remnants by electronically scanning portions of computer memory of at least one computer system of one or more of computing nodes, the portions of computer memory being allocated to persistent storage of computer data, and each container remnant containing computer data providing a record of system-generated execution attributes generated in response to execution of one or more containerized applications. One or more inactive container remnants unutilized by a currently running containerized application can be identified among the plurality of container remnants. Each inactive container remnant can be categorized, the categorizing being based on system-generated execution attributes contained in each inactive container remnant.

Abstract: Containerized software discover and identification can include discovering a plurality of container remnants by electronically scanning portions of computer memory of at least one computer system of one or more of computing nodes, the portions of computer memory being allocated to persistent storage of computer data, and each container remnant containing computer data providing a record of system-generated execution attributes generated in response to execution of one or more containerized applications. One or more inactive container remnants unutilized by a currently running containerized application can be identified among the plurality of container remnants. Each inactive container remnant can be categorized, the categorizing being based on system-generated execution attributes contained in each inactive container remnant.

Abstract: A user input is received. The user input is classified using a neural network. A set of contextual information for the user input is determined, based on the classification. The user input is modified, based on the classification and the set of contextual information. The modified user input is passed to a virtual assistant.

Abstract: Provided are techniques for automated software application license usage. Global detection results that indicate, for each of a plurality of end user computers, one or more installed software applications, one or more running software applications, and a user account. Unique pairs are identified using the global detection results, wherein each pair of the unique pairs comprises a software application name for a software application and a user name for a user. For a unique pair, a query is run against a user registry to get a list of user groups that the user name is a member of, a user group is selected that corresponds to the software application name, and a number of users in the selected user group is designated as a license demand for authorized users for the software application. Then, based on the global detection results and the license demand, one or more recommendations are provided.

Abstract: Providing a method for real-time testing and monitoring of a business process management system in order to identify commonly failing parameters. Machine learning methods are used to run multiple rounds of testing of the business process management system to more accurately identify commonly failing parameters and to modify the business process by removing those identified parameters from future rounds of testing.

Abstract: Technology for logging application usage based on monitoring of read or change operations on database elements and then using the information from log data to determine application usage states (for example, active, inactive) in time.

Abstract: A first computer system is scanned at configured, default time intervals by a software asset management (“SAM”) software application installed on the first computer system, wherein the scanning detects other software applications installed on the first computer system. The SAM software application generates a list of the other software applications installed on the first computer system and stores the list on a computer readable storage medium. An override software module triggers the SAM software application to perform an early scan of the first computer system, before a scan of the first computer system that the SAM software application would otherwise perform at a next default scan time. The triggering is in response to information obtained from a source external to the first computer system about a triggering one of the software applications on the list.

Abstract: An example operation may include one or more of detecting a web session within an open window of a web browser on a user device, monitoring for user activity on the user device with respect to the web browser, determining there has been a lack of user activity with respect to the web browser for a predetermined amount of time based on the monitoring of the user activity, and temporarily concealing content within the open window of the web browser in response to the lack of user activity.

Abstract: In an approach for providing adaptive software inventory scan frequencies and schedules, a processor receives information from an initial scan of a set of software inventory scans, wherein the information includes at least one of: running processes, file system entries, registry entries, and software catalog evaluations. A processor analyzes the information from the initial scan. A processor predicts an outcome for future software inventory scans based on the analysis of the information, wherein the prediction includes a scanning frequency and a scanning schedule.

Abstract: In an approach for providing adaptive software inventory scan frequencies and schedules, a processor receives information from an initial scan of a set of software inventory scans, wherein the information includes at least one of: running processes, file system entries, registry entries, and software catalog evaluations. A processor analyzes the information from the initial scan. A processor predicts an outcome for future software inventory scans based on the analysis of the information, wherein the prediction includes a scanning frequency and a scanning schedule.

Abstract: A method and a related system for identifying unmatched registry entries may be provided. The method may comprise scanning a file system and discovering software based on a file signature, collecting first attributes of the discovered software, collecting native registry entries, and comparing the first attributes against second attributes of the collected registry entries based on a filtering rule. Thereby, the registry entries may be grouped into two groups. One group may represent matched registry entries and the other group may represent unmatched registry entries. The unmatched registry entries may be identified as unequivocal entries for further software discovery.

Abstract: Installation information of a software component is received. The software component is software. The installation information includes an installation state for the software component, an identifier for the software component, a vendor of the software component, and a calendar date of the installation. The installation information is stored in a pseudo-permanent database. The record of the installation information in the pseudo-permanent database will survive the uninstallation of the software component. An update to the installation information is received in response to the installation state of the software component changing. The update includes modified installation information and a calendar date of when the installation information changed. The updated installation information is stored in the pseudo-permanent database.

Abstract: Identification of unmatched registry entries may be provided, by scanning a file system, discovering software, collecting first attribute values of the discovered software, receiving a plurality of filtering rules including a method and an attribute. The attribute may comprise a software-specific condition. The method may further comprise collecting native registry entries comprising second attribute values indicated by said attributes of at least one of said rule, and comparing said first attribute values of said discovered software with related ones of said second attribute values of said collected native registry entries. Then, the native registry entries may be grouped into two groups. The first group represents matched registry entries and the second group represents unmatched registry entries. The unmatched registry entries may be identified as unequivocal entries for further software discovery.

Abstract: A method and a related system for identifying unmatched registry entries may be provided. The method may comprise scanning a file system and discovering software based on a file signature, collecting first attributes of the discovered software, collecting native registry entries, and comparing the first attributes against second attributes of the collected registry entries based on a filtering rule. Thereby, the registry entries may be grouped into two groups. One group may represent matched registry entries and the other group may represent unmatched registry entries. The unmatched registry entries may be identified as unequivocal entries for further software discovery.

Abstract: A method and a related system for identifying unmatched registry entries may be provided. The method may comprise scanning a file system and discovering software based on a file signature, collecting first attributes of the discovered software, collecting native registry entries, and comparing the first attributes against second attributes of the collected registry entries based on a filtering rule. Thereby, the registry entries may be grouped into two groups. One group may represent matched registry entries and the other group may represent unmatched registry entries. The unmatched registry entries may be identified as unequivocal entries for further software discovery.

Abstract: Software asset management (SAM) for virtual environments performing the following operations: (i) collecting a set of virtual machine (VM) descriptor(s) sets, where each VM descriptor(s) set corresponds to one of the following: a VM appliance or a sealed VM image; (ii) for each VM descriptor(s) set of the set of VM descriptor(s) set(s), determining whether there is a match to an entry in a VM descriptor set/software application mapping data set; and (iii) for each VM descriptor(s) set of the set of VM descriptor(s) set(s), on condition that there is a match, determining an identity of a set of software application(s) in the VM appliance or sealed VM image corresponding to the VM descriptor(s) set based on the respectively matching entry in the VM descriptor/software mapping data set.