Abstract: Data migration of an application from source to target information system is a critical step for a successful modernization project. There are few commercial tools available to address the data migration challenge, creation of a data transformation specification is largely a manual, knowledge intensive, and expert driven process. A system and method for learning based synthesis of data transformation rules have been provided. The system is focused on automating important aspects of automatic inference of the transformation specification. The key principles behind the system and method are derived from the observations on how experts use domain, system, and historical mapping knowledge while creating data transformation specifications. The system contains two major components, schema matching and transformation rule program generation. The system uses machine learning, knowledge representation for schema matching and developed rule generator using a deductive synthesizer.

Abstract: Data migration of an application from source to target information system is a critical step for a successful modernization project. There are few commercial tools available to address the data migration challenge, creation of a data transformation specification is largely a manual, knowledge intensive, and expert driven process. A system and method for learning based synthesis of data transformation rules have been provided. The system is focused on automating important aspects of automatic inference of the transformation specification. The key principles behind the system and method are derived from the observations on how experts use domain, system, and historical mapping knowledge while creating data transformation specifications. The system contains two major components, schema matching and transformation rule program generation. The system uses machine learning, knowledge representation for schema matching and developed rule generator using a deductive synthesizer.

Abstract: A method and system for assigning an annotation to a statement in a source code. The method comprises generating intermediate representation of the source code by parsing the source code. The method comprises identifying one or more instances of definition of a variable and one or more instances of use of the variable. The method comprises categorizing the variable into a group of variables based on the one or more instances of definition of the variable, the one or more instances of use of the variable, a description of the variable, and mathematical operators defining a correlation between the variable and one or more other variables. Further, a data description table and a data dictionary of the plurality of variables are created. The method assigns an annotation to the variable present in the statement of the source code based on the data description table and the data dictionary.

Abstract: Disclosed is a method and system for assigning an annotation to a statement in a source code. The method comprises generating intermediate representation of the source code by parsing the source code. The method comprises identifying one or more instances of definition of a variable and one or more instances of use of the variable. The method comprises categorizing the variable into a group of variables based on the one or more instances of definition of the variable, the one or more instances of use of the variable, a description of the variable, and mathematical operators defining a correlation between the variable and one or more other variables. Further, a data description table and a data dictionary of the plurality of variables are created. The method assigns an annotation to the variable present in the statement of the source code based on the data description table and the data dictionary.