Abstract: Aspects of the disclosure relate to message compliance analysis. A computing platform may access historical messages. The computing platform may pre-process the historical messages to configure the historical messages for use in training a disclaimer model to identify whether or not input messages include a disclaimer. The computing platform may train, using the pre-processed historical messages, the disclaimer model. The computing platform may receive a new message. The computing platform may input, into the disclaimer model, the new message, which may produce a disclaimer score indicating a likelihood that the new message includes a disclaimer. The computing platform may compare the disclaimer score to a disclaimer threshold. Based on identifying that the disclaimer score meets or exceeds the disclaimer threshold, the computing platform may remove, from a set of messages scheduled for compliance review, the new message, and send, to an intended recipient of the new message, the new message.

Abstract: Aspects of the disclosure relate to using a machine learning system to process a corpus of documents associated with a user to determine a user-specific consequence index. A computing platform may load a corpus of documents associated with a user. Subsequently, the computing platform may create a first plurality of smart groups based on the corpus of documents, and then may generate a first user interface comprising a representation of the first plurality of smart groups. Next, the computing platform may receive user input applying one or more labels to a plurality of documents associated with at least one smart group. Subsequently, the computing platform may create a second plurality of smart groups based on the corpus of documents and the received user input. Then, the computing platform may generate a second user interface comprising a representation of the second plurality of smart groups.

Abstract: Aspects of the disclosure relate to using a machine learning system to process a corpus of documents associated with a user to determine a user-specific consequence index. A computing platform may load a corpus of documents associated with a user. Subsequently, the computing platform may create a first plurality of smart groups based on the corpus of documents, and then may generate a first user interface comprising a representation of the first plurality of smart groups. Next, the computing platform may receive user input applying one or more labels to a plurality of documents associated with at least one smart group. Subsequently, the computing platform may create a second plurality of smart groups based on the corpus of documents and the received user input. Then, the computing platform may generate a second user interface comprising a representation of the second plurality of smart groups.

Abstract: Aspects of the disclosure relate to using a machine learning system to process a corpus of documents associated with a user to determine a user-specific consequence index. A computing platform may load a corpus of documents associated with a user. Subsequently, the computing platform may create a first plurality of smart groups based on the corpus of documents, and then may generate a first user interface comprising a representation of the first plurality of smart groups. Next, the computing platform may receive user input applying one or more labels to a plurality of documents associated with at least one smart group. Subsequently, the computing platform may create a second plurality of smart groups based on the corpus of documents and the received user input. Then, the computing platform may generate a second user interface comprising a representation of the second plurality of smart groups.

Abstract: An application integration driver infrastructure for facilitating the use of a distributed directory running in a computer network is provided. The infrastructure can transform specific directory events into a vendor-neutral data identification system and then use vendor-neutral transformation technologies to transform the neutral data identification into a specific application's data format, and vice-versa. The infrastructure receives an event from the distributed directory into a markup language generation system, such as an extensible markup language (“XML”) generator. The XML generator converts the event into XML data and provides the XML data to a transformation processing system, such as an extensible transformation language (“XSLT”) processor. The XSLT processor transforms the XML data to a predetermined format. The format can be dictated by a stylesheet provided to the XSLT processor, the stylesheet being responsive to requirements of a computer application.

Abstract: An application integration driver infrastructure for facilitating the use of a distributed directory running in a computer network is provided. The infrastructure can transform specific directory events into a vendor-neutral data identification system and then use vendor-neutral transformation technologies to transform the neutral data identification into a specific application's data format, and vice-versa. The infrastructure receives an event from the distributed directory into a markup language generation system, such as an extensible markup language (“XML”) generator. The XML generator converts the event into XML data and provides the XML data to a transformation processing system, such as an extensible transformation language (“XSLT”) processor. The XSLT processor transforms the XML data to a predetermined format. The format can be dictated by a stylesheet provided to the XSLT processor, the stylesheet being responsive to requirements of a computer application.

Abstract: An improved system, method and database program is provided for automatically creating a replica of data distributed throughout a database onto a replica server. The method identifies an action to be performed on the data and/or a subset of objects of the data for receiving the action. A scope of the distributed database is determined, including a root of the database for accessing all of the data. With this information, a vector is created and provided to the root. As a result, the root can recursively inherit the vector down the distributed database to one or more branches until all of the data is accessed, so that the action can be performed on all the data.