Abstract: Embodiments of the invention provide systems and methods for logging of messages in a development environment. More specifically, embodiments of the present invention provide dynamically adaptive logging of runtime messages generated by an application. These embodiments provide a way to handle the volume of information stored in the logs by dynamically changing the severity associated with generated messages based on previous code path execution. Embodiments can use a set of metrics to replace the usual static log level associated with the code by the developer. For example, such metrics can include but are not limited to a cost-based (storage volume on disk), an exception-based (weight increase in catch block), and/or a crowd-based (community can vote down noise). As a result, embodiments can provide more detailed information when the error is recurring for a particular user but without generating so much information as to make the log difficult to use.

Abstract: Embodiments of the invention provide systems and methods for logging of messages in a development environment. More specifically, embodiments of the present invention provide dynamically adaptive logging of runtime messages generated by an application. These embodiments provide a way to handle the volume of information stored in the logs by dynamically changing the severity associated with generated messages based on previous code path execution. Embodiments can use a set of metrics to replace the usual static log level associated with the code by the developer. For example, such metrics can include but are not limited to a cost-based (storage volume on disk), an exception-based (weight increase in catch block), and/or a crowd-based (community can vote down noise). As a result, embodiments can provide more detailed information when the error is recurring for a particular user but without generating so much information as to make the log difficult to use.

Abstract: A neuroagent approach is used in an automated and unified data mining system to provide an explicitly predictive knowledge model. The neuroagent is a neural multi-agent approach based on macro-connectionism and comprises a double integration at the association and symbolic level as well as the knowledge model level. This data mining system permits discovery, evaluation and prediction of the correlative factors of data, i.e., the conjunctions, as corresponding to neuroexpressions (a semantic connection of neuroagents) connected to an output neuroagent which corresponds to the data output, the connection weights yielding the relative significance of these factors to the given output.