Abstract: A method and system for providing access to a resource. A request for an individual within an organization to access the resource is received. In response to the request having been received, at least one constraint for accessing the resource by the individual is ascertained, based on respective constraints for accessing the resource by one or more other individuals in the organization. The one or more other individuals have a same role in the organization as the individual or have a respective relationship with the individual. The at least one constraint is provided to the individual.

Abstract: A system for developing a simulation of a process. In one aspect, a system creates a first model within the simulation. The first model represents a part of the process and comprises a first port to which other models may be connected. The system also creates a second model within the simulation. The second model represents another part of the process and comprises a second port to which other models may be connected. The system then connects the first port and the second port together. Upon connection, the system allocates a memory location as a connection variable that represents a type of information transfer between the first and second ports. A first port variable, which represents a value transferrable through the first port, is set to reference the value at the allocated memory location. Similarly, a second port variable, which represents a value transferrable through the second port, is also set to reference the value at the allocated memory location.

Abstract: A camera module comprises a supporting member for supporting a sensor chip to move relative to a lens for correcting camera shake, a plurality of bulges is provided between the supporting member and a bottom plate. The bulges provide a reduced resistance to sliding on the supporting member. An electronic device using the module is also disclosed.

Abstract: Machine-learning-based, adaptive updating of quantitative data in a database system is provided, which includes training one or more machine learning models to facilitate adaptively updating quantitative data in the database system, and ascertaining an update urgency index for updating the quantitative data for one or more data structures of the database system. The update urgency index is representative of an urgency for updating the quantitative data for the data structure(s) and is based, at least in part, on real-time query metrics. The machine learning model(s) is used to adaptively update the quantitative data, where the adaptively updating is based, at least in part, on the ascertained update urgency index. Processing of a database query is optimized in the database system using the adaptively updated quantitative data.

Abstract: Various embodiments include a method for deploying field device into an Internet of Things (IoT). The method may include: acquiring information from a field device using an edge device; transmitting the acquired information to a cloud platform; wherein the information comprises data and an industrial IoT model; converting the industrial IoT model into a graph; performing similarity analysis based on the graph; classifying the industrial IoT model based on the similarity analysis; generating a first industrial IoT model comprising a type or an example; performing data mapping on the first industrial IoT model; and operating the field device as part of the IoT.

Abstract: Various embodiments described herein include an application program development and deployment method comprising: acquiring, from a code template library, a first code template matching a code structure of an application program to be developed; creating a template instance based on the first code template; filling a custom code input by a developer into the template instance to obtain a source program code matching said application program; compiling the source program code to obtain a target program code; acquiring a target operating environment mirror matching the target program code from an environment mirror library, wherein at least one operating environment mirror is stored in the environment mirror library, and different operating environment mirrors correspond to different operating environments; and deploying the target program code and the target operating environment mirror onto a target cloud platform.

Abstract: Various embodiments of the teachings herein include a method for generating a virtual Internet-of-things device on the basis of an Internet-of-things model. The method may include: obtaining a first number of Internet-of-things models for an Internet-of-things solution; subjecting a variable in the first number of Internet-of-things models to variable attribute expansion to add an extended variable attribute to the variable, wherein the variable subjected to variable attribute expansion is capable of data simulation; configuring variable link relations and variable link processing rules for the extended Internet-of-things models to obtain an Internet-of-things model configuration file; and obtaining a second number of virtual Internet-of-things devices on the basis of a virtual physical network device architecture by using the Internet-of-things model configuration file.

Abstract: Machine-learning-based, adaptive updating of quantitative data in a database system is provided, which includes training one or more machine learning models to facilitate adaptively updating quantitative data in the database system, and ascertaining an update urgency index for updating the quantitative data for one or more data structures of the database system. The update urgency index is representative of an urgency for updating the quantitative data for the data structure(s) and is based, at least in part, on real-time query metrics. The machine learning model(s) is used to adaptively update the quantitative data, where the adaptively updating is based, at least in part, on the ascertained update urgency index. Processing of a database query is optimized in the database system using the adaptively updated quantitative data.

Abstract: Computer technology for use in a computer system including a database system including a database management sub-system, a primary database and a secondary database, with the secondary database being maintained at least approximately in synchronization with the primary database over time as changes are made to data of the primary database (for example, a database system that includes disaster recovery (DR) features). A statistics update is performed first on a standby database to determine whether query efficiency and/or query optimization really will be substantially improved by performance of the statistics update. If not, then statistics update on the primary database is postponed until a future time.

Abstract: A field data transmission method comprises: a cloud platform determining at least one first device operation index be obtained via data analysis. For each first device operation index, the cloud platform generates control information for the first device operation index. The control information is used to determine a primary edge controller from among at least one edge controller, wherein the primary edge controller is used to send first field data to the cloud platform, the first field data is used for data analysis by the cloud platform to obtain the first device operation index, and the first field data is obtained by the primary edge controller preprocessing second field data. The cloud platform sends each piece of control information to each edge controller, respectively. The cloud platform receives first field data from each primary edge controller, respectively.

Abstract: Machine-learning-based, adaptive updating of quantitative data in a database system is provided, which includes training one or more machine learning models to facilitate adaptively updating quantitative data in the database system, and ascertaining an update urgency index for updating the quantitative data for one or more data structures of the database system. The update urgency index is representative of an urgency for updating the quantitative data for the data structure(s) and is based, at least in part, on real-time query metrics. The machine learning model(s) is used to adaptively update the quantitative data, where the adaptively updating is based, at least in part, on the ascertained update urgency index. Processing of a database query is optimized in the database system using the adaptively updated quantitative data.

Abstract: Various embodiments include a method for deploying field device into an Internet of Things (IoT). The method may include: acquiring information from a field device using an edge device; transmitting the acquired information to a cloud platform; wherein the information comprises data and an industrial IoT model; converting the industrial IoT model into a graph; performing similarity analysis based on the graph; classifying the industrial IoT model based on the similarity analysis; generating a first industrial IoT model comprising a type or an example; performing data mapping on the first industrial IoT model; and operating the field device as part of the IoT.

Abstract: The present disclosure provides a method and device for assessing a lifecycle of a component. In an embodiment, the method includes: obtaining, using a simulation device, model description information that corresponds to component models of some or all components in the production line; obtaining at least one piece of data information that is needed by at least one component model that corresponds to the model description information; performing event marking based on the event information on the obtained data information; obtaining component status information of a corresponding component of each model based on the marked data information; and generating a corresponding analysis report based on the component status information from. An advantage of an embodiment is that, a failure mode, a key module, and a measurement rule no longer rely on manual operations and experience of an expert. Instead corresponding information may be precisely provided by using a PLM system.

Abstract: Various embodiments of the teachings herein include a method for generating a virtual Internet-of-things device on the basis of an Internet-of-things model. The method may include: obtaining a first number of Internet-of-things models for an Internet-of-things solution; subjecting a variable in the first number of Internet-of-things models to variable attribute expansion to add an extended variable attribute to the variable, wherein the variable subjected to variable attribute expansion is capable of data simulation; configuring variable link relations and variable link processing rules for the extended Internet-of-things models to obtain an Internet-of-things model configuration file; and obtaining a second number of virtual Internet-of-things devices on the basis of a virtual physical network device architecture by using the Internet-of-things model configuration file.

Abstract: Various embodiments described herein include an application program development and deployment method comprising: acquiring, from a code template library, a first code template matching a code structure of an application program to be developed; creating a template instance based on the first code template; filling a custom code input by a developer into the template instance to obtain a source program code matching said application program; compiling the source program code to obtain a target program code; acquiring a target operating environment mirror matching the target program code from an environment mirror library, wherein at least one operating environment mirror is stored in the environment mirror library, and different operating environment mirrors correspond to different operating environments; and deploying the target program code and the target operating environment mirror onto a target cloud platform.

Abstract: The present disclosure relates to a method for configuring a priority level, a cloud platform, a system, a computing device, and a medium. The method for configuring a priority level for data on a cloud platform comprises determining the data type of received data; determining, according to the data type, a priority level specification used for describing a priority level of the received data; and calculating the priority level of the received data on the basis of the priority level specification of the received data.

Abstract: A field data transmission method comprises: a cloud platform determining at least one first device operation index be obtained via data analysis. For each first device operation index, the cloud platform generates control information for the first device operation index. The control information is used to determine a primary edge controller from among at least one edge controller, wherein the primary edge controller is used to send first field data to the cloud platform, the first field data is used for data analysis by the cloud platform to obtain the first device operation index, and the first field data is obtained by the primary edge controller preprocessing second field data. The cloud platform sends each piece of control information to each edge controller, respectively. The cloud platform receives first field data from each primary edge controller, respectively.

Abstract: An application configuration method includes: obtaining a resource recommendation prompt from a repository according to a user customized demand of an industrial Internet of Things solution, invoking a resource from a knowledge base or the repository to generate an industrial Internet of Things model, and matching an application installed at an industrial cloud and at an edge device end with a configuration needed by the application; and generating the industrial Internet of Things solution and deploying the matched application to the industrial cloud and the edge device end.

Abstract: The present disclosure relates to an aqueous basecoat material including as binder, at least one aqueous, acrylate-based microgel dispersion (MD), and also at least one pigment paste including at least one color and/or effect pigment, and also as paste binder, at least one polymer of olefinically unsaturated monomers, where the polymer includes functional groups for nonionic stabilization of the polymer in water, and also functional groups selected from the group of silicon-containing, phosphorus-containing, and urea-containing groups, the aqueous basecoat material further including less than 0.5 wt %, based on its total weight, of synthetic phyllosilicates.

Abstract: A system for developing a simulation of a process. In one aspect, a system creates a first model within the simulation. The first model represents a part of the process and comprises a first port to which other models may be connected. The system also creates a second model within the simulation. The second model represents another part of the process and comprises a second port to which other models may be connected. The system then connects the first port and the second port together. Upon connection, the system allocates a memory location as a connection variable that represents a type of information transfer between the first and second ports. A first port variable, which represents a value transferrable through the first port, is set to reference the value at the allocated memory location. Similarly, a second port variable, which represents a value transferrable through the second port, is also set to reference the value at the allocated memory location.