Abstract: Software generates interfaces and functionality for an application program. At least a portion of an application is created by given names to data elements according to a syntax. Data tables are generated having fields that correspond to the elements and characteristics are associated with the fields specifying how the data in the fields interact with each other. Relationships among the elements are inferred as a function of their names. A functional interface can be generated from the inferred relationship.

Abstract: Software generates interfaces and functionality for an application program. At least a portion of an application is created by given names to data elements according to a syntax. Data tables are generated having fields that correspond to the elements and characteristics are associated with the fields specifying how the data in the fields interact with each other. Relationships among the elements are inferred as a function of their names. A functional interface can be generated from the inferred relationship.

Abstract: A system and method automatically identify candidate helpdesk problem categories that are most amenable to automated solutions. The system generates a dictionary wherein each word in the text data set is identified, and the number of documents containing these words is counted, and a corresponding count is generated. The documents are partitioned into clusters. For each generated cluster, the system sorts the dictionary terms in order of decreasing occurrence frequency. It then determines a search space by selecting the top dictionary terms as specified by a user defined depth of search. Next, the system chooses a set of terms from the search space as specified by a user-defined value indicating the desired level of detail.

Abstract: A system and method automatically identify candidate helpdesk problem categories that are most amenable to automated solutions. The system generates a dictionary wherein each word in the text data set is identified, and the number of documents containing these words is counted, and a corresponding count is generated. The documents are partitioned into clusters. For each generated cluster, the system sorts the dictionary terms in order of decreasing occurrence frequency. It then determines a search space by selecting the top dictionary terms as specified by a user defined depth of search. Next, the system chooses a set of terms from the search space as specified by a user-defined value indicating the desired level of detail.