Abstract: In one implementation, a data transformation engine is employed to analyze an incoming JSON schema syntax and use a “best effort” model to convert a plurality of JSON schemas to other types of schemas such as an XML schema. The model is configured to be adaptable and dynamic in order to convert as much of the JSON as possible. In addition, the conversion tool is configured to transform schemas to other technologies and allow for conversion errors within one or more error thresholds. Further, the system may be distributed standalone with no dependencies to other implementations, such as BPM implementations.

Abstract: In implementations, a schema generation utility is configured to allow a user to abstract and generate a schema, such as a JSON schema, from a base schema instance according to schema configuration data, other data inputs, and schema configuration models. In an implementation, a user provides parameters, configuration objects, etc. for a desired final output schema. The schema generation utility may be configured to receive and parse the base schema instance into a plurality of configurable data objects, also referred to as “schema chunks,” that are extracted from the base schema instance. A set of schema chunks may be assembled into a desired output schema based on, at least in part, configuration data and assembly data contained in the schema chunks. In other implementations, input parameters may include error tolerance thresholds processed by the schema generator to discern between errors that lead to schema failure and benign errors.

Abstract: In implementations, a schema generation utility is configured to allow a user to abstract and generate a schema, such as a JSON schema, from a base schema instance according to schema configuration data, other data inputs, and schema configuration models. In an implementation, a user provides parameters, configuration objects, etc. for a desired final output schema. The schema generation utility may be configured to receive and parse the base schema instance into a plurality of configurable data objects, also referred to as “schema chunks,” that are extracted from the base schema instance. A set of schema chunks may be assembled into a desired output schema based on, at least in part, configuration data and assembly data contained in the schema chunks. In other implementations, input parameters may include error tolerance thresholds processed by the schema generator to discern between errors that lead to schema failure and benign errors.

Abstract: In one implementation, a data transformation engine is employed to analyze an incoming JSON schema syntax and use a “best effort” model to convert a plurality of JSON schemas to other types of schemas such as an XML schema. The model is configured to be adaptable and dynamic in order to convert as much of the JSON as possible. In addition, the conversion tool is configured to transform schemas to other technologies and allow for conversion errors within one or more error thresholds. Further, the system may be distributed standalone with no dependencies to other implementations, such as BPM implementations.

Abstract: In one implementation, a data transformation engine is employed to analyze an incoming JSON schema syntax and use a “best effort” model to convert a plurality of JSON schemas to other types of schemas such as an XML schema. The model is configured to be adaptable and dynamic in order to convert as much of the JSON as possible. In addition, the conversion tool is configured to transform schemas to other technologies and allow for conversion errors within one or more error thresholds. Further, the system may be distributed standalone with no dependencies to other implementations, such as BPM implementations.

Abstract: A system and method for enhancing web application performance by selectively caching information used by different stages of server-side processing of a request for webpage content from a client system. An example method involves using a specialized cache requestor for selectively fetching and caching JavaBean properties once per request lifecycle. The cached JavaBeans properties may then be used by different request-processing stages of the lifecycle, thereby obviating the need to implement multiple external requests for JavaBeans properties, i.e., multiple accesses of dynamic context. The cache requestor may leverage functionality provided by a JSF phase listener (JSF phaseListener) and a JavaBean proxy (BeanProxy) to keep the retrieved state information accessible to the request processing and associated JSF container.

Abstract: Implementations include a resource compatibility engine and method configured to verify resource compatibility when importing schemas and formats such as XSD and WSDL into a database table such as a business catalog. The resource compatibility engine may be configured to prevent possible catalog corruption from importing file types that do not match. The resource compatibility engine may also be employed to identify which are the resource file types that need to be overwritten in case of an update and provides useful feedback in case the import is not successful.

Abstract: In implementations, a schema generation utility is configured to allow a user to abstract and generate a schema, such as a JSON schema, from a base schema instance according to schema configuration data, other data inputs, and schema configuration models. In an implementation, a user provides parameters, configuration objects, etc. for a desired final output schema. The schema generation utility may be configured to receive and parse the base schema instance into a plurality of configurable data objects, also referred to as “schema chunks,” that are extracted from the base schema instance. A set of schema chunks may be assembled into a desired output schema based on, at least in part, configuration data and assembly data contained in the schema chunks. In other implementations, input parameters may include error tolerance thresholds processed by the schema generator to discern between errors that lead to schema failure and benign errors.

Abstract: A system and method for enhancing web application performance by selectively caching information used by different stages of server-side processing of a request for webpage content from a client system. An example method involves using a specialized cache requestor for selectively fetching and caching JavaBean properties once per request lifecycle. The cached JavaBeans properties may then be used by different request-processing stages of the lifecycle, thereby obviating the need to implement multiple external requests for JavaBeans properties, i.e., multiple accesses of dynamic context. The cache requestor may leverage functionality provided by a JSF phase listener (JSF phaseListener) and a JavaBean proxy (BeanProxy) to keep the retrieved state information accessible to the request processing and associated JSF container.

Abstract: Implementations include a resource compatibility engine and method configured to verify resource compatibility when importing schemas and formats such as XSD and WSDL into a database table such as a business catalog. The resource compatibility engine may be configured to prevent possible catalog corruption from importing file types that do not match. The resource compatibility engine may also be employed to identify which are the resource file types that need to be overwritten in case of an update and provides useful feedback in case the import is not successful.

Abstract: In one implementation, a data transformation engine is employed to analyze an incoming JSON schema syntax and use a “best effort” model to convert a plurality of JSON schemas to other types of schemas such as an XML schema. The model is configured to be adaptable and dynamic in order to convert as much of the JSON as possible. In addition, the conversion tool is configured to transform schemas to other technologies and allow for conversion errors within one or more error thresholds. Further, the system may be distributed standalone with no dependencies to other implementations, such as BPM implementations.