Abstract: A system includes a memory that stores computer executable components and neural network data, and a processor executes computer executable components stored in the memory. An assessment component assesses a computer network, and classifies the computer network relative to M network classifications stored in a repository, wherein M is an integer greater than one. A risk component determines risk of vulnerability subject to change impact regarding protection against a computer virus or cyber-attack based on historical information regarding vulnerability exposure and vulnerability remediation changes relative to the classification of the computer network. A recommendation component that generates recommendations and best action to mitigate risk and impact, and remediate the vulnerabilities based on the risk assessment and business priorities.

Abstract: A method and system for determining a driver is provided. The method includes receiving and analyzing GPS data identifying locations of GPS enabled devices of a plurality of users. A group of users are determined to be located within a specified proximity to each other based on the analysis. The group of users are determined, based on an altitude, velocity, and a vector of the group of users, to be located within a vehicle. A position and role for each user with respect to the vehicle is determined. Selected functions of each GPS enabled device are controlled based on each role.

Abstract: A method, computer system, and computer program product for determining whether data compression should be performed at a storage virtualization layer or a backend layer, within a storage area network, including assessing a volume of data to decide whether the volume of data should be compressed or is already compressed, and in response to deciding that the volume of data should be compressed: checking whether a restriction exists on the volume of data, and in response to deciding that the volume of data is already compressed: determining that the volume of data already compressed should be uncompressed based on at least one metric.

Abstract: Techniques that facilitate group patching recommendation and/or remediation with risk assessment are provided. In one example, a system includes a vertical stack component, a horizontal stack component and a risk classification component. The vertical stack component identifies a first patch profile from a software system associated with a computer system environment. The horizontal stack component identifies a second patch profile from a hardware system associated with network nodes of the computer system environment. The system learns over time to identify repetitive patterns using machine learning techniques. Then, the risk classification component performs a machine learning process to determine a risk classification for the computer system environment based on the first patch profile and the second patch profile.

Abstract: A method and system for determining a driver is provided. The method includes receiving and analyzing GPS data identifying locations of GPS enabled devices of a plurality of users. A group of users are determined to be located within a specified proximity to each other based on the analysis. The group of users are determined, based on an altitude, velocity, and a vector of the group of users, to be located within a vehicle. A position and role for each user with respect to the vehicle is determined. Selected functions of each GPS enabled device are controlled based on each role.

Abstract: A method and system for determining a driver is provided. The method includes receiving and analyzing GPS data identifying locations of GPS enabled devices of a plurality of users. A group of users are determined to be located within a specified proximity to each other based on the analysis. The group of users are determined, based on an altitude, velocity, and a vector of the group of users, to be located within a vehicle. A position and role for each user with respect to the vehicle is determined. Selected functions of each GPS enabled device are controlled based on each role.

Abstract: A method, computer system, and computer program product for determining whether data compression should be performed at a storage virtualization layer or a backend layer, within a storage area network, including assessing a volume of data to decide whether the volume of data should be compressed or is already compressed, and in response to deciding that the volume of data should be compressed: checking whether a restriction exists on the volume of data, and in response to deciding that the volume of data is already compressed: determining that the volume of data already compressed should be uncompressed based on at least one metric.

Abstract: A system includes a memory that stores computer executable components and neural network data, and a processor executes computer executable components stored in the memory. An assessment component assesses a computer network, and classifies the computer network relative to M network classifications stored in a repository, wherein M is an integer greater than one. A risk component determines risk of vulnerability subject to change impact regarding protection against a computer virus or cyber-attack based on historical information regarding vulnerability exposure and vulnerability remediation changes relative to the classification of the computer network. A recommendation component that generates recommendations and best action to mitigate risk and impact, and remediate the vulnerabilities based on the risk assessment and business priorities.

Abstract: A method and system of identifying a computing device vulnerability is provided. Social media communication is monitored. Social media threads that are related to a vulnerability, based on the monitored social media communication, are identified, filtered, and categorized into one or more predetermined categories of computing device vulnerabilities. Upon determining that a number of social media posts related to the vulnerability is above a first predetermined threshold, one or more dependable social media threads in a same one or more categories as the vulnerability are searched. One or more possible root causes of the vulnerability are determined from the searched dependable social media threads. A validity score for each of the one or more possible root causes is assigned. A possible root cause from that has a highest validity score that is above a second predetermined threshold is selected to be the root cause of the vulnerability.

Abstract: Techniques facilitating relational patch orchestration based on impact analysis are provided. In one example, a computer-implemented method comprises creating, by a device operatively coupled to a processor, patch execution plans for one or more pending patches associated with a computing environment; quantifying, by the device, impact of respective ones of the patch execution plans based on dependencies associated with the respective ones of the patch execution plans; and optimizing, by the device, a patch execution plan from the patch execution plans based on the impact of the respective ones of the patch execution plans.

Abstract: A method and system of identifying technical experts for an identified vulnerability is provided. One or more technical experts for each of one or more categories of the vulnerability are identified. Questions are sent to and answers are received from the one or more identified technical experts for each of the one or more categories of vulnerabilities, via a chatbot module. Answers to parameters that are missing for a Common Vulnerability Scoring System (CVSS) for the identified vulnerability are determined from the received answers to the parameters. The answers to the parameters are validated and a CVSS score is calculated based on the validated determined answers.

Abstract: Techniques that facilitate group patching recommendation and/or remediation with risk assessment are provided. In one example, a system includes a vertical stack component, a horizontal stack component and a risk classification component. The vertical stack component identifies a first patch profile from a software system associated with a computer system environment. The horizontal stack component identifies a second patch profile from a hardware system associated with network nodes of the computer system environment. The system learns over time to identify repetitive patterns using machine learning techniques. Then, the risk classification component performs a machine learning process to determine a risk classification for the computer system environment based on the first patch profile and the second patch profile.

Abstract: A method and system for determining a driver is provided. The method includes receiving and analyzing GPS data identifying locations of GPS enabled devices of a plurality of users. A group of users are determined to be located within a specified proximity to each other based on the analysis. The group of users are determined, based on an altitude, velocity, and a vector of the group of users, to be located within a vehicle. A position and role for each user with respect to the vehicle is determined. Selected functions of each GPS enabled device are controlled based on each role.

Abstract: A method and system for determining a driver is provided. The method includes receiving and analyzing GPS data identifying locations of GPS enabled devices of a plurality of users. A group of users are determined to be located within a specified proximity to each other based on the analysis. The group of users are determined, based on an altitude, velocity, and a vector of the group of users, to be located within a vehicle. A position and role for each user with respect to the vehicle is determined. Selected functions of each GPS enabled device are controlled based on each role.

Abstract: A method and system for determining a driver is provided. The method includes receiving and analyzing GPS data identifying locations of GPS enabled devices of a plurality of users. A group of users are determined to be located within a specified proximity to each other based on the analysis. The group of users are determined, based on an altitude, velocity, and a vector of the group of users, to be located within a vehicle. A position and role for each user with respect to the vehicle is determined. Selected functions of each GPS enabled device are controlled based on each role.

Abstract: A method and system for determining a driver is provided. The method includes receiving and analyzing GPS data identifying locations of GPS enabled devices of a plurality of users. A group of users are determined to be located within a specified proximity to each other based on the analysis. The group of users are determined, based on an altitude, velocity, and a vector of the group of users, to be located within a vehicle. A position and role for each user with respect to the vehicle is determined. Selected functions of each GPS enabled device are controlled based on each role.

Abstract: A method, system, and computer usable program product for relating code with intellectual property assets are provided in the illustrative embodiments. An intellectual property asset is identified. A portion of code is located from an entire code of a software application, such that the portion of code corresponds to the intellectual property asset. The portion of code is marked with information identifying the intellectual property asset. The portion of the code is executed during the execution of the software application. The execution of the portion of code is recorded, the recording indicating a use of the intellectual property asset. Additionally, a set of analysis control factors may be accepted. The recording may be analyzed according to the set of analysis control factors. A recommendation for an intellectual property asset related business decision to develop, invest in, or maintain certain intellectual property assets may be produced.

Abstract: Embodiments described herein provide recovery placeholders within an application. Specifically, one approach includes providing an application operating on a client device, and generating a recovery placeholder that defines a current state of the application by analyzing a queue containing a set of messages, and identifying one or more selected events corresponding to the application from the queue. In one approach, the current state defines, at the time the recovery placeholder is generated, at least one of: a position within a window of the application, a current activity of the application, a position of the window within a display of a display device, and a placement order of the window of the application in relation to a stack of other cascaded windows. At a later point in time, the application may then be restored to the current state by accessing the recovery placeholder to replay the one or more selected events.

Abstract: Embodiments described herein provide recovery placeholders within an application. Specifically, one approach includes providing an application operating on a client device, and generating a recovery placeholder that defines a current state of the application by analyzing a queue containing a set of messages, and identifying one or more selected events corresponding to the application from the queue. In one approach, the current state defines, at the time the recovery placeholder is generated, at least one of: a position within a window of the application, a current activity of the application, a position of the window within a display of a display device, and a placement order of the window of the application in relation to a stack of other cascaded windows. At a later point in time, the application may then be restored to the current state by accessing the recovery placeholder to replay the one or more selected events.

Abstract: Embodiments described herein provide recovery placeholders within an application. Specifically, one approach includes providing an application operating on a client device, and generating a recovery placeholder that defines a current state of the application by analyzing a queue containing a set of messages, and identifying one or more selected events corresponding to the application from the queue. In one approach, the current state defines, at the time the recovery placeholder is generated, at least one of: a position within a window of the application, a current activity of the application, a position of the window within a display of a display device, and a placement order of the window of the application in relation to a stack of other cascaded windows. At a later point in time, the application may then be restored to the current state by accessing the recovery placeholder to replay the one or more selected events.