Abstract: An embodiment for managing machine learning models to generate and utilize perforations within machine learning models to improve their ability to consider and learn from exception decisions. The embodiment may detect an exception decision in a base model. The embodiment may automatically determine data considered in making the exception decision and identify and store in a database known features from the gathered data. The embodiment may automatically identify and store in the database remaining additional features considered, and generate and store perforations corresponding to the remaining additional features considered. The embodiment may, in response to detecting subsequent decisions involving shared additional features contained in the generated perforations, automatically validate feature boundaries within the generated perforations from a set of data sources.

Abstract: An approach is presented for specifying categories of data elements during a service specification phase of a service-oriented architecture (SOA) life cycle defined in a service modeling methodology like Service-Oriented Modeling and Architecture (SOMA). A Unified Modeling Language based SOA modeling tool for the service modeling methodology includes a middleware based integration plug-in that categorizes service-specific data elements as transaction elements, glue elements, core Common Information Model (CIM) elements, and elements extending the CIM elements, and associates the categorized data elements with corresponding operations of the service being modeled.