Systems and methods are described for training a large language model with query auto-completion training data and automatically generating query auto-completion training data in an interactive GUI. A computing system continuously trains and refines a large language model utilizing masking techniques to on complex software-related queries. The computing system is further configured to utilize the large language model to provide complex software-related query suggestions to users operating a graphical user interface real-time.

Systems and methods are provided for a computing system in a cloud computing environment to receive a request for planning data via a user interface of a computing device accessing a planning application executing in the cloud computing environment and to execute queries corresponding to the request for planning data against an SAP Business Warehouse Integrated Planning Cube (BW cube). The computing system loads data received from the executed queries into an application layer of the planning application executing in the cloud computing environment and stores each manipulation action to the loaded data in the application layer of the planning application executing in the cloud computing environment without persisting any data to the BW cube. The computing system persists the updated data to the BW cube only upon detecting completion of the manipulation actions.

Embodiments disclosed herein provide for systems and methods of a reporting engine framework that dynamically generates a database query. The systems and methods provide for generating the database query via an intuitive user interface, wherein the user interface interacts with a plurality of application program interfaces to retrieve and generate data associated with the database to be queried.

Systems and methods are presented herein for increasing user engagement with an interface by suggesting commands or queries for the user. A plurality of content items available for consumption are identified and metadata for each of the plurality of content items is retrieved. One or more candidate voice commands are generated based on a plurality of voice command templates based on a target verb and a subset of the metadata corresponding to the plurality of the content items available for consumption. A recall score is generated for each candidate voice command based at least in part on a detection of phonetic features that match between clauses of each candidate voice command. At least the candidate voice command with the highest recall score is selected and output using a suggestion system.

A data processing system for producing a subset of data from a plurality of data sources, including: memory storing a plurality of data sources to be represented in an editor interface; a data structure modification module that selects a plurality of data sources to be represented in an editor interface and generates a subset of data included in the plurality of data sources; memory that stores the selected data structures included in the subset, with at least one of the stored data structures including the one or more modified attributes of the one or more respective fields; rendering module that displays, in the editor interface, representations of the stored data structures; and a segmentation modules that segments a plurality of received data records.

The present disclosure relates to systems and methods for an intelligent assistant (e.g., a chatbot) that can be used to enable a user to generate a machine learning system. Techniques can be used to automatically generate a machine learning system to assist a user. In some cases, the user may not be a software developer and may have little or no experience in either machine learning techniques or software programming. In some embodiments, a user can interact with an intelligent assistant. The interaction can be aural, textual, or through a graphical user interface. The chatbot can translate natural language inputs into a structural representation of a machine learning solution using an ontology. In this way, a user can work with artificial intelligence without being a data scientist to develop, train, refine, and compile machine learning models as stand-alone executable code.

Techniques are described herein for generating, editing, and optimizing queries using neural networks. In some embodiments, the techniques include training a neural network using a set of performant database queries to automatically learn patterns between different sequences of tokens in performant queries. Once trained, the neural network may receive an incomplete query as input, where the incomplete query includes one or more query tokens. The trained neural network may then perform next token prediction to project a set of one or more additional query tokens that may follow the one or more query tokens in the incomplete query to form a completed, performant query.

Methods for generating data models using a generative adversarial network can begin by receiving a data model generation request by a model optimizer from an interface. The model optimizer can provision computing resources with a data model. As a further step, a synthetic dataset for training the data model can be generated using a generative network of a generative adversarial network, the generative network trained to generate output data differing at least a predetermined amount from a reference dataset according to a similarity metric. The computing resources can train the data model using the synthetic dataset. The model optimizer can evaluate performance criteria of the data model and, based on the evaluation of the performance criteria of the data model, store the data model and metadata of the data model in a model storage. The data model can then be used to process production data.

Systems and methods for generating a data aggregator interactive graphical user interface. An interactive graphical user interface (GUI) includes a selectable symbol region, a query region and a data results region. The selectable symbol region displays predefined symbols. The query region displays user input fields for generating queries. The system receives user input associated with the user input fields of the query region to form a filter set. The data results region is automatically updated responsive to the user input, to display one or more data values from among one or more databases associated with the filter set. The system receives a subscription request to save the filter set as a user-customized query. A custom symbol is created responsive to the subscription request that is associated with the filter set. The the selectable symbol region is updated to display the custom symbol together with the predefined symbols.

Embodiments are directed towards real time display of event records and extracted values based on at least one extraction rule, such as a regular expression. A user interface may be employed to enable a user to have an extraction rule automatically generate and/or to manually enter an extraction rule. The user may be enabled to manually edit a previously provided extraction rule, which may result in real time display of updated extracted values. The extraction rule may be utilized to extract values from each of a plurality of records, including event records of unstructured machine data. Statistics may be determined for each unique extracted value, and may be displayed to the user in real time. The user interface may also enable the user to select at least one unique extracted value to display those event records that include an extracted value that matches the selected value.