Abstract: A computing server receives, through a message communication channel from a user, a documentation of a transaction. The computing server parses data in the documentation to create a data instance representing the transaction in a database. The computing server determines that the data instance needs an assignment of a category from a list of custom-defined categories. The computing server applies a machine-learned encoder model to features of the data instance to determine candidate categories from the list of custom-defined categories. The computing server transmits a response message to the user through the message communication channel. The response message includes at least one of the candidate categories determined by the machine learning model. Responsive to transmitting the response message to the user, the computing server receives feedback from the user. The computing server generates a category assignment for the data instance based on the user feedback.

Abstract: A computing server receives, through a message communication channel from a user, a documentation of a transaction. The computing server parses data in the documentation to create a data instance representing the transaction in a database. The computing server determines that the data instance needs an assignment of a category from a list of custom-defined categories. The computing server applies a machine-learned encoder model to features of the data instance to determine candidate categories from the list of custom-defined categories. The computing server transmits a response message to the user through the message communication channel. The response message includes at least one of the candidate categories determined by the machine learning model. Responsive to transmitting the response message to the user, the computing server receives feedback from the user. The computing server generates a category assignment for the data instance based on the user feedback.

Abstract: A computing server retrieves a list of custom-defined categories of a database and accesses a plurality of training samples for training a machine-learned encoder model. The computing server trains the machine-learned encoder model that generates embeddings of data instances. The machine-learned encoder model is trained to separate a plurality of embeddings of positive data instances belong to the target category from a plurality of embeddings of negative data instances. The computing server receives a target data instance that is to be imported to the third-party data platform and generates features of the target data instance to prepare the target data instance for further processing. The computing server applies the machine-learned encoder model to the target data instance to determine an assignment of a category from the list of custom-defined categories. The computing server exports the target data instance including the determined assignment to the third-party data platform.

Abstract: An apparatus may receive, through a user interface, an input indicative of a document corpus from which to indicate trends of a document set. The document corpus may be one of a plurality of document corpuses included in the document set. The apparatus may filter the document corpus based on user-specific criteria to provide a filtered document corpus. The filtered document corpus includes a subset of documents from the document corpus. The apparatus may output an indication of the trends of the document set based on trending text of the filtered document corpus, where the trends of the document set correspond to the trending text of the filtered document corpus.

Abstract: An apparatus may map one or more fields from a plurality of tables having a first table type to one or more mapped fields of a single table having a second table type and index the one or more mapped fields to correspond to a single index for the single table having the second table type. The apparatus may execute a first query using the single index corresponding to the one or more mapped fields to output a result for the first query. The apparatus may execute a second query for search activity that has occurred on the database since a last update to the single index and combine the result for the first query with the search activity that has occurred on the database since the last update to the single index to output a combined result based on the second query.

Abstract: An apparatus may map one or more fields from a plurality of tables having a first table type to one or more mapped fields of a single table having a second table type and index the one or more mapped fields to correspond to a single index for the single table having the second table type. The apparatus may execute a first query using the single index corresponding to the one or more mapped fields to output a result for the first query. The apparatus may execute a second query for search activity that has occurred on the database since a last update to the single index and combine the result for the first query with the search activity that has occurred on the database since the last update to the single index to output a combined result based on the second query.