Abstract: A computing system configured to perform artificial intelligence-powered search and evaluation functionalities is disclosed. The computer system can receive a natural language search input entered through a user interface, request a search engine to perform a search based on the natural language search input, receive search results returned by the search engine, and submit, in runtime, structured data contained in the search results to a large language model. The structured data includes a table including one or more records and a plurality of fields. The computing system can prompt, in runtime, the large language model to generate scores for the one or more records and present the structured data and the scores on the user interface.

Abstract: A method includes receiving a data set. The data set includes a plurality of data subsets wherein each data subset is associated with one transaction in a fully or partially masked network. The method further includes processing each data subset according to a plurality of rules to generate a plurality of activation values and an output for the each data subset. The plurality of activation values and the output for the each data subset form an activation pattern for the each data subset. The method also includes generating a predictive model based on the activation patterns. The method further includes identifying a subset of transactions as outliers based on the predictive model.

Abstract: A method includes receiving a data set. The data set includes a plurality of data subsets wherein each data subset is associated with one transaction in a fully or partially masked network. The method further includes processing each data subset according to a plurality of rules to generate a plurality of activation values and an output for the each data subset. The plurality of activation values and the output for the each data subset form an activation pattern for the each data subset. The method also includes generating a predictive model based on the activation patterns. The method further includes identifying a subset of transactions as outliers based on the predictive model.

Abstract: A method includes processing data sets according to a plurality of rules to generate an activation pattern for each data set. Each activation pattern includes an activation value for each rule of the plurality of rules. The method also includes normalizing the activation value for each rule and determining a standard deviation of the activation value for each rule. The method further includes identifying a first subset of rules of the plurality of rules. Each rule of the first subset of rules has activation value with the standard deviation greater than a standard deviation threshold. The method also includes identifying, using an unsupervised machine learning algorithm, outlier activation patterns and analyzing the outlier activation patterns based on a second subset of rules of the plurality of rules. The second subset of rules is a subset of the first subset of rules.

Abstract: A system and method for facilitating interoperability of data transformations developed in different programming platforms under a unified platform including receiving a first transformation utilizing a first programming platform; receiving information about the first transformation; wrapping the first transformation; including the wrapped, first transformation in a transformation pipeline, the transformation pipeline including a second transformation that is wrapped, the second transformation utilizing a second programming platform different from the first programming platform; and executing the transformation pipeline including the wrapped, first transformation and the wrapped, second transformation in batch mode or real-time streaming mode.

Abstract: A system and method for exporting a training model for performing a prediction including sending for presentation, to a user, a directed acyclic graph representing a model trained using a transformed source dataset and one or more transformations used to obtain the transformed source dataset; determining the model as an endpoint based on a user selection; determining a transformation as a start point; determining whether one or more intervening transformations exist on a path going from the start point and leading to the endpoint in the directed acyclic graph; and exporting the model and relevant transformations, the relevant transformations including the transformation at the start point and any intervening transformations on the path going from the start point and leading to the model at endpoint in the directed acyclic graph to a production environment.