Abstract: A method and associated systems for automatically generating an ontology and a set of axioms from a business-process model that represents the operations of a business. This ontology and set of axioms may be used to create the knowledgebase of an artificially intelligent expert system that emulates the business operations. A processor parses a representation of business processes stored in the business-process model, deriving a set of axioms and a set of entity classes from the parsed data. The processor uses these axioms and classes to identify concept nodes and process nodes, which it organizes into the ontology of the knowledgebase. The processor further identifies information derived from the parsed data to create a set of triple data items, each of which represents the information represented by one or more of the derived axioms. These triples are stored in the knowledgebase as a triple store data structure.

Abstract: A method and associated systems for automating natural-language interactions between an expert system and a user. Characteristics of and relationships among elements of a set of business processes are captured, organized into a data structure, and stored as a set of business process models. The format, contents, and organization of these models are chosen to facilitate translating characteristics of the business processes into a format that may be stored in a knowledgebase of an expert system. The structured business-process models are then translated into a component of a knowledgebase from which an expert system may infer rules that guide its natural-language user interactions that relate to the business processes. In some embodiments, a similar method may be used to automatically translate business-process metadata, such as concept classes and process classes, into a similar knowledgebase-compatible format.

Abstract: A method and associated systems for automatically generating an ontology and a set of axioms from a business-process model that represents the operations of a business. This ontology and set of axioms may be used to create the knowledgebase of an artificially intelligent expert system that emulates the business operations. A processor parses a representation of business processes stored in the business-process model, deriving a set of axioms and a set of entity classes from the parsed data. The processor uses these axioms and classes to identify concept nodes and process nodes, which it organizes into the ontology of the knowledgebase. The processor further identifies information derived from the parsed data to create a set of triple data items, each of which represents the information represented by one or more of the derived axioms. These triples are stored in the knowledgebase as a triple store data structure.

Abstract: A method of running a query for a database having partitioned tables. The method includes loading data into a table partition; forecasting statistics for the table partition based on previously gathered partition statistics using a data mining algorithm; and subsequently to forecasting statistics, running a query by a query optimizer; and wherein the method is performed by one or more computing devices. Also disclosed is a computer program product and a system.

Abstract: A method and system for optimizing a database query. A database table populated with data is received and scanned. Statistics and single column histograms associated with single columns of the table are determined. Cardinality based on the statistics and histograms is estimated. All possible correlations among multiple columns are determined by performing clustering data mining that partitions data in the table into clusters. Top ranked columns based on the correlations are determined. The difference between the estimated cardinality and a support count of a cluster is determined to exceed a threshold, and in response, multiple column histograms based on the top ranked columns are determined. An optimal query plan based on the multiple column histograms is generated.