Abstract: The disclosure generally describes computer-implemented methods, software, and systems, including a method for generating executable components. One method includes identifying a user request to create a new function based pre-existing algorithms, the new function to be used in an application used by a user; providing a set of available algorithms from an algorithm library; receiving a selection by a user of an algorithm from the available algorithms; providing a set of available parameters associated with the selected algorithm; receiving an election by the user of one or more parameters from the set of available parameters; generating an executable component in response to receiving the selection of the algorithm and the election of the one or more parameters, the executable component performing the selected algorithm using at least the elected one or more parameters; and storing the executable component for subsequent execution in response to the requested new function.

Abstract: Data sets are analyzed to discover trends and determine predictive data models. A data model is determined based on analyzing the data set with a specific algorithm from a set of different analytical algorithms. The data model is stored in a structural file format. The data model is converted into an instantiated object model with the use of a predefined object model. The instantiated object model is converted into an in-database analysis model to score new data within a database system. The scoring is based on the logic in the data model. The in-database analysis model is stored on a database server part of a database system to provide analytical functionality defined in the data model. The new data can be scored with the in-database analysis model. The new data is not extracted from the database system and the scoring is accomplished in-memory.

Abstract: The disclosure generally describes computer-implemented methods, software, and systems, including a method for generating executable components. One method includes identifying a user request to create a new function based pre-existing algorithms, the new function to be used in an application used by a user; providing a set of available algorithms from an algorithm library; receiving a selection by a user of an algorithm from the available algorithms; providing a set of available parameters associated with the selected algorithm; receiving an election by the user of one or more parameters from the set of available parameters; generating an executable component in response to receiving the selection of the algorithm and the election of the one or more parameters, the executable component performing the selected algorithm using at least the elected one or more parameters; and storing the executable component for subsequent execution in response to the requested new function.

Abstract: Data sets are analyzed to discover trends and determine predictive data models. A data model is determined based on analyzing the data set with a specific algorithm from a set of different analytical algorithms. The data model is stored in a structural file format. The data model is converted into an instantiated object model with the use of a predefined object model. The instantiated object model is converted into an in-database analysis model to score new data within a database system. The scoring is based on the logic in the data model. The in-database analysis model is stored on a database server part of a database system to provide analytical functionality defined in the data model. The new data can be scored with the in-database analysis model. The new data is not extracted from the database system and the scoring is accomplished in-memory.

Abstract: Various embodiments of systems and methods for performing predictive analysis are described herein. In one aspect, the method includes receiving a command for publishing a chain comprising a plurality of components connected together to perform predictive analysis. Based upon the command, a plurality of procedures corresponding to the plurality of components of the chain is generated. The generated procedures are integrated according to an order of connectivity of the components within the chain. A database object including the integrated procedures is generated. The database object is stored within a database. The stored database object is executable for performing predictive analysis.

Abstract: A workflow execution framework is generated to execute a received workflow. The workflow is semantically analyzed to determine workflow chain and associated workflow components. To execute the workflow chain, a terminal component in the workflow chain and a corresponding sequential hierarchy of the workflow components are detected. A result descriptor of a data source component corresponding to the terminal component is computed and stored in an execution state table. Result descriptors are computed for the workflow components succeeding the data source component in the sequential hierarchy and are stored in the execution state table. Upon detecting a dataflow between the data source component and one of the succeeding workflow components, data along each row of the execution state table is extracted to process the one of the succeeding workflow components. The workflow is executed by processing the workflow components associated with the workflow chain, thereby executing the workflow chain.

Abstract: Various embodiments of systems and methods for executing in-database data mining processes are described herein. In one aspect, the method includes identifying a newly created chain comprising a plurality of components connected together to perform a data mining task, generating an identifier (ID) for the newly created chain, identifying metadata associated with the chain, and storing the ID and the metadata related to the newly created chain into a repository. Each component comprises a parameterized script including one or more parameters. Values of the parameters are stored in the repository. The parameters within the scripts are replaced by their corresponding values and the components of the chain are executed sequentially to generate a final output.