Abstract: Described herein are machine learning methods and systems for application programming Interface (API) management. One or more machine learning-based models, such as a neural network, may be trained to provide a prediction(s) that an API request is likely to succeed (e.g., not cause the API to crash) or to fail (e.g., cause the API to crash, freeze, etc.). The one or more machine learning-based models may be trained using historical API requests that were successful and historical API requests that are not successful. Various aspects of the API requests, as well as system properties associated with the machine(s) that executes/processes the historical API requests, may be used as well when training the one or more machine learning-based models.

Abstract: Technologies are provided for database modification using a script component. In some embodiments, a method comprises executing, by a computing system comprising at least one processor, a script component comprising one or more functions to transform a database. The method also includes modifying, by the computing system, the database based on at least one transformation defined by the one or more functions in response to executing the script component.

Abstract: Technologies are provided for detection and mitigation of high-risk online activity. The detection and mitigation can be implemented in real-time. In some embodiments, a computing system can determine that a risk assessment for an online activity is unavailable from an in-memory storage. The computing system can obtain the risk assessment for the online activity from a second computing system configured to apply a prediction model to data defining the online activity. The risk assessment can comprise a risk score and a risk category. The computing system can update the in-memory storage to incorporate the data and the risk assessment, and can determine that the risk assessment is indicative of the online activity being high-risk activity. The computing system can then cause denial of access to a computing platform to a user device associated with the online activity.

Abstract: Technologies are provided for database modification using a script component. In some embodiments, a method comprises executing, by a computing system comprising at least one processor, a script component comprising one or more functions to transform a database. The method also includes modifying, by the computing system, the database based on at least one transformation defined by the one or more functions in response to executing the script component.

Abstract: Methods and systems for determining insights from source data associated with an entity are described herein. The insights may be based on a difference(s) between source data associated with an entity, which may represent real/historical data, and input data associated with the entity, which may be separate from the source data. Images of user interface objects, such as charts or graphs representative of one or more metrics associated with the entity, may be analyzed using a machine-learning model to determine such insights. For example, additional user interface objects indicative of the difference(s) between the source data and the input data may be generated by the machine-learning model using imaging data and imaging metadata associated with images of the charts or graphs representative of the one or more metrics data.

Abstract: Described herein are machine learning methods and systems for application programming Interface (API) management. One or more machine learning-based models, such as a neural network, may be trained to provide a prediction(s) that an API request is likely to succeed (e.g., not cause the API to crash) or to fail (e.g., cause the API to crash, freeze, etc.). The one or more machine learning-based models may be trained using historical API requests that were successful and historical API requests that are not successful. Various aspects of the API requests, as well as system properties associated with the machine(s) that executes/processes the historical API requests, may be used as well when training the one or more machine learning-based models.

Abstract: Technologies are provided for composition of a query via a user interface and for visualization of data responsive to the query. For example, based on interactions with a user interface, a data model may be defined. The interactions may be drag-and-drop interactions with the user interface. Based on the data model and a data context, the query may be determined and executed.

Abstract: Technologies are provided for detection and mitigation of high-risk online activity. The detection and mitigation can be implemented in real-time. In some embodiments, a computing system can determine that a risk assessment for an online activity is unavailable from an in-memory storage. The computing system can obtain the risk assessment for the online activity from a second computing system configured to apply a prediction model to data defining the online activity. The risk assessment can comprise a risk score and a risk category. The computing system can update the in-memory storage to incorporate the data and the risk assessment, and can determine that the risk assessment is indicative of the online activity being high-risk activity. The computing system can then cause denial of access to a computing platform to a user device associated with the online activity.

Abstract: Technologies are provided for database modification using a script component. In some embodiments, a method comprises executing, by a computing system comprising at least one processor, a script component comprising one or more functions to transform a database. The method also includes modifying, by the computing system, the database based on at least one transformation defined by the one or more functions in response to executing the script component.

Abstract: Methods and systems for processing data are disclosed. In an aspect, a first set of data can be received. A first set of intermediate data can be determined based on the first set of data. A basis for a factorization can be determined based on the first set of intermediate data. A second set of data can be received. The basis for the factorization can be updated based on the second set of data. The factorization can be performed based on the updated basis for the factorization.

Abstract: Methods and systems for processing data are disclosed. In an aspect, a first set of data can be received. A first set of intermediate data can be determined based on the first set of data. A basis for a factorization can be determined based on the first set of intermediate data. A second set of data can be received. The basis for the factorization can be updated based on the second set of data. The factorization can be performed based on the updated basis for the factorization.