User interfaces are provided for improved data optimization. A model user interface can be used to generate models based on a historical data file based on modeling details and filters specified by a user. The user can save the models and apply the models to optimize a data file. The user can specify optimization details and see visualizations of the results.

A system and method of the disclosure relates to structuring at least one resource of an application programming interface (API) for a server. In the method, a plurality of field-level elements may be defined. Each of the plurality of field-level elements may be associated with a respective property. Also, first and second message-level elements may be defined. The first message-level element may be based on a first field-level element of the plurality of field-level elements, and the second message-level element may be based on the first field-level element. The API may be associated with a first resource having properties resolved based on at least the first and second message-level elements.

An event processing system for processing events in an event stream is disclosed. The system is configured for determining a stage for a continuous query language (CQL) query being processed by an event processing system and/or determining a stage type associated with the stage. The system is also configured for determining a transformation to be computed for the stage based at least in part on the stage type and/or determining a classification for the CQL query based at least in part on a plurality of rules. The system can also be configured for generating a transformation in a Directly Acyclic Graph (DAG) of a data transformation pipeline for the stage based at least in part on the partitioning criteria for the stage. In some examples, the system can also be configured for determining a partitioning of the stage based at least in part on the transformation.

An event processing system for processing events in an event stream is disclosed. The system is configured for determining a stage for a continuous query language (CQL) query being processed by an event processing system and/or determining a stage type associated with the stage. The system is also configured for determining a transformation to be computed for the stage based at least in part on the stage type and/or determining a classification for the CQL query based at least in part on a plurality of rules. The system can also be configured for generating a transformation in a Directly Acyclic Graph (DAG) of a data transformation pipeline for the stage based at least in part on the partitioning criteria for the stage. In some examples, the system can also be configured for determining a partitioning of the stage based at least in part on the transformation.

User interfaces are provided for improved data optimization. A model user interface can be used to generate models based on a historical data file based on modeling details and filters specified by a user. The user can save the models and apply the models to optimize a data file. The user can specify optimization details and see visualizations of the results.

The subject technology transforms a machine learning model into a transformed machine learning model in accordance with a particular model specification when the machine learning model does not conform to the particular model specification, the particular model specification being compatible with an integrated development environment (IDE). The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model. Further, the subject technology provides the generated code interface and the code for display in the IDE, the IDE enabling modifying of the generated code interface and the code.

The subject technology transforms a machine learning model into a transformed machine learning model in accordance with a particular model specification when the machine learning model does not conform to the particular model specification, the particular model specification being compatible with an integrated development environment (IDE). The subject technology generates a code interface and code for the transformed machine learning model, the code interface including code statements in the object oriented programming language, the code statements corresponding to an object representing the transformed machine learning model. Further, the subject technology provides the generated code interface and the code for display in the IDE, the IDE enabling modifying of the generated code interface and the code.

Technologies are described herein for generating a service application. A service application generator can be used to generate a service application upon receiving a prompt to generate the service application. The service application generator can interface with a user or other entity to determine information used to build a service application.

Technologies are described herein for generating a service application. A service application generator can be used to generate a service application upon receiving a prompt to generate the service application. The service application generator can interface with a user or other entity to determine information used to build a service application.

One embodiment provides for generating a cognitive executable process graph including obtaining, by a processor, a hybrid process knowledge graph generated based process fragments and a set of actionable statements and business constraints. The hybrid process knowledge graph including different node types. The hybrid knowledge graph is traversed from a root of a process through each task in the hybrid process knowledge graph to obtain an action and metadata for each task node. Based on the action and metadata, at least one statement in an equivalent executable code block is created to represent the action. A cognitive executable process graph is generated based on at least one executable code block.