Abstract: A method performed by a multi-subscriber identity module (MUSIM) user equipment (UE) for continuing operation of the MUSIM UE in a first mode, the MUSIM UE including a first SIM and a second SIM, and the method including transmitting, by the MUSIM UE, an uplink signalling message in response to determining a trigger condition has been satisfied, the uplink signalling message including one of compatible band information for the first SIM and the SIM, or one or more other stacks serving band information associated with the second SIM, receiving, by the MUSIM UE from a network, a radio resource control (RRC) reconfiguration signalling message for performing at least one activity at the first SIM in response to the transmitting the uplink signalling message, and performing, by the MUSIM UE, the at least one activity based on the RRC reconfiguration signalling message.

Abstract: A method of storing semi-structured data includes receiving user data from a user of a query system where the user data includes semi-structured user data. The method also includes receiving an indication that the semi-structured user data fails to include a fixed schema. In response to the indication that the semi-structured user data fails to include the fixed schema, the method further includes parsing the semi-structured user data into a plurality of data paths and extracting a data type associated with each respective data path of the plurality of data paths. The method additionally includes storing the semi-structured user data as a row entry in a table of a database in communication with the query system wherein each column value associated with the row entry corresponds to a respective one of the plurality of data paths and the data type associated with the respective data path.

Abstract: A method of storing semi-structured data includes receiving user data from a user of a query system where the user data includes semi-structured user data. The method also includes receiving an indication that the semi-structured user data fails to include a fixed schema. In response to the indication that the semi-structured user data fails to include the fixed schema, the method further includes parsing the semi-structured user data into a plurality of data paths and extracting a data type associated with each respective data path of the plurality of data paths. The method additionally includes storing the semi-structured user data as a row entry in a table of a database in communication with the query system wherein each column value associated with the row entry corresponds to a respective one of the plurality of data paths and the data type associated with the respective data path.

Abstract: A method of storing semi-structured data includes receiving user data from a user of a query system where the user data includes semi-structured user data. The method also includes receiving an indication that the semi-structured user data fails to include a fixed schema. In response to the indication that the semi-structured user data fails to include the fixed schema, the method further includes parsing the semi-structured user data into a plurality of data paths and extracting a data type associated with each respective data path of the plurality of data paths. The method additionally includes storing the semi-structured user data as a row entry in a table of a database in communication with the query system wherein each column value associated with the row entry corresponds to a respective one of the plurality of data paths and the data type associated with the respective data path.

Abstract: Monitoring knowledge is distilled into platform-nonspecific patterns of high-level language elements compiled into management packs or other rule packs targeting specific platforms. A server health and/or configuration monitoring knowledge compiler accepts distillation document(s) and target-specific information, and generates target-specific rule packs to be consumed by monitoring tools to monitor specific target platforms consistent with the platform-nonspecific monitoring elements. Computational rule pack generation is qualitatively different from manual rule pack creation. Plug-ins tailor the compiler to generate on-premises or cloud-based rule packs. Distillation element examples include monitor alert types, instructional content types, target attributes and tags, monitored item types, event alert types, performance collection types, policy monitor types, and threshold monitor types.

Abstract: A virtual lab is generated for a underlying learning objective defined by a lab instructor. The virtual lab is decomposed into multiple lab steps. Each lab step defines a portion of the underlying learning objective that can be performed independently. Each lab step includes pedagogical information for teaching a learner the portion of the underlying learning objective and information for specifying a portion of the underlying system that will execute the lab step. A first portion of the underlying system is caused to execute a first lab step. This allows the learner to perform the portion of the underlying learning objective defined in the first lab step. An event generated by the learner's performance of the first lab step is received. In response, a specific pedagogical procedure that is dictated by the event is caused to be performed.

Abstract: Authenticating a badge. The badge represents at least one of skills, training, attributes, or qualifications of an individual. The method includes at a trustworthy verifier, accessing a badge image identified by a user. The method further includes at the trustworthy verifier, accessing policy identified by the user. The method further includes determining that the badge identified by the user is compliant with the policy by determining that the badge complies with the policy identified by the user. As a result of determining that the badge is compliant with the policy, the method further includes causing an indicator to be displayed in a trustworthy way to indicate to the user that the purported badge is compliant with the policy.

Abstract: Identifying that one or more badges in a set of a plurality of badges indicates that an individual having the set of a plurality of badges meets certain requirements in terms of one or more of skills, training, attributes, or qualifications. The method includes identifying a set of a plurality of badges for an individual. The method further includes identifying evaluation criteria, the evaluation criteria comprising criteria for evaluating a plurality of badges, that when satisfied, indicates that an individual meets certain requirements. The method further includes comparing the set of the plurality of badges to the evaluation criteria.

Abstract: Sending alerts regarding events related to badges. A method includes receiving a subscription for an entity to receive alerts regarding one or more badges or one or more individuals as it relates to the one or more individuals receiving or maintaining badges. The one or more badges signify one or more of skills, training, attributes, or qualifications of individuals who receive them. The method further includes determining that an event has occurred with respect to the one or more badges or one or more individuals. As a result, the method further includes notifying the entity of the event.

Abstract: Monitoring knowledge is distilled into platform-nonspecific patterns of high-level language elements compiled into management packs or other rule packs targeting specific platforms. A server health and/or configuration monitoring knowledge compiler accepts distillation document(s) and target-specific information, and generates target-specific rule packs to be consumed by monitoring tools to monitor specific target platforms consistent with the platform-nonspecific monitoring elements. Computational rule pack generation is qualitatively different from manual rule pack creation. Plug-ins tailor the compiler to generate on-premises or cloud-based rule packs. Distillation element examples include monitor alert types, instructional content types, target attributes and tags, monitored item types, event alert types, performance collection types, policy monitor types, and threshold monitor types.