Abstract: A method for generating and modifying a datastore may include identifying all processes associated with an observation model and identifying any metrics associated with each process to be recorded. The method may also include automatically constructing a database schema to store the metric data and provide appropriate interrelations between the processes.

Abstract: A method for generating and modifying a datastore may include identifying all processes associated with an observation model and identifying any metrics associated with each process to be recorded. The method may also include automatically constructing a database schema to store the metric data and provide appropriate interrelations between the processes.

Abstract: Development of domain-specific analytic applications having predefined data mining models, including identifying a business problem to be solved; selecting a data mining algorithm appropriate for solving the business problem; defining data schema for use as inputs and outputs to and from the mining algorithm, the data schema including input data schema and output data schema; and defining a mining model based on the data schema to solve the business problem; whereby a domain-specific analytic application is developed, the analytic application having at least one predefined data mining model, the analytic application having the capabilities of: populating the input data schema with sample historical data; production training the mining model using the sample historical data in the input data schema, whereby a production trained mining model is created; and production scoring production data using the production trained mining model, production scoring further comprising applying the production trained mining m

Abstract: Automated data mining using domain-specific analytic applications for solving predefined problems, including populating input data schema, the input data schema having a format appropriate to solution of a predefined problem. Production training a predefined data mining model to produce a trained data mining model, the predefined data mining model comprising a predefined data mining model definition, production training having an output of a knowledge base. Executing a preselected data mining algorithm in production training mode. Production scoring input data from the input data schema. The method typically includes scheduling the steps of populating input data schema, production training, and production scoring. Typically the analytic application includes predefined problems, predefined data mining algorithms, predefined data schema, and at least one predefined data mining model definition.

Abstract: Automated data mining using domain-specific analytic applications for solving predefined problems, including populating input data schema, the input data schema having a format appropriate to solution of a predefined problem. Production training a predefined data mining model to produce a trained data mining model, the predefined data mining model comprising a predefined data mining model definition, production training having an output of a knowledge base. Executing a preselected data mining algorithm in production training mode. Production scoring input data from the input data schema. The method typically includes scheduling the steps of populating input data schema, production training, and production scoring. Typically the analytic application includes predefined problems, predefined data mining algorithms, predefined data schema, and at least one predefined data mining model definition.

Abstract: Automated data mining using domain-specific analytic applications for solving predefined problems, including populating input data schema, the input data schema having a format appropriate to solution of a predefined problem. Production training a predefined data mining model to produce a trained data mining model, the predefined data mining model comprising a predefined data mining model definition, production training having an output of a knowledge base. Executing a preselected data mining algorithm in production training mode. Production scoring input data from the input data schema. The method typically includes scheduling the steps of populating input data schema, production training, and production scoring. Typically the analytic application includes predefined problems, predefined data mining algorithms, predefined data schema, and at least one predefined data mining model definition.

Abstract: Automated data mining using domain-specific analytic applications for solving predefined problems, including populating input data schema, the input data schema having a format appropriate to solution of a predefined problem. Production training a predefined data mining model to produce a trained data mining model, the predefined data mining model comprising a predefined data mining model definition, production training having an output of a knowledge base. Executing a preselected data mining algorithm in production training mode. Production scoring input data from the input data schema. The method typically includes scheduling the steps of populating input data schema, production training, and production scoring. Typically the analytic application includes predefined problems, predefined data mining algorithms, predefined data schema, and at least one predefined data mining model definition.

Abstract: Development of domain-specific analytic applications having predefined data mining models, including identifying a business problem to be solved; selecting a data mining algorithm appropriate for solving the business problem; defining data schema for use as inputs and outputs to and from the mining algorithm, the data schema including input data schema and output data schema; and defining a mining model based on the data schema to solve the business problem; whereby a domain-specific analytic application is developed, the analytic application having at least one predefined data mining model, the analytic application having the capabilities of: populating the input data schema with sample historical data; production training the mining model using the sample historical data in the input data schema, whereby a production trained mining model is created; and production scoring production data using the production trained mining model, production scoring further comprising applying the production trained mining m