Abstract: Systems and methods for moving, reconciling, and aggregating data from mainframe computers to hybrid cloud are disclosed.

Abstract: Systems and methods for providing secure access to delegated resources are disclosed. In accordance with embodiments, a method may include receiving a delegation request, where the delegation request is associated with a user account; determining a list of delegable resources for which the user account has permission to delegate access; presenting the list of delegable resources via a user interface; receiving from the user interface, a user selection of a delegable resource from the list; generating a delegate identifier; associating an access permission to the delegate identifier, where the access permission provides access to the delegable resource; receiving the delegate identifier via the user interface; authenticating the delegate identifier; and providing access to the delegable resource based on the access permission.

Abstract: Systems and methods for moving, reconciling, and aggregating data from mainframe computers to hybrid cloud are disclosed.

Abstract: Systems and methods for determining an amount of architecture drift are disclosed. In one aspect, a method comprises determining a logical architecture node. The logical architecture node is in operation on a technology infrastructure of an evaluating organization. The logical architecture node can be included as an architecture graph node that represents the logical architecture node in a knowledge graph. A functional attribute of the logical architecture node and an intended attribute of the logical architecture node can be determined and included in the knowledge graph as a functional attribute graph node and an intended attribute graph node, respectively. The functional attribute graph node and the intended attribute graph node can be of the same type but can have different values. An amount of architecture drift can be determined based on the difference in the values of the functional attribute graph node and the intended attribute graph node.

Abstract: Systems and methods for identifying and remediating architecture risk are disclosed. In one aspect, a method includes generating a first chaos graph pattern and a second chaos graph pattern; training a machine learning model to recognize the first chaos graph pattern and the second chaos graph pattern; identifying an architecture graph pattern of an evaluated architecture; including the architecture graph pattern in an architecture testing graph; recognizing by the machine learning model that a shape of the architecture graph pattern is similar to a shape of the first chaos graph pattern and that the shape of the architecture graph pattern is similar to a shape of the second chaos graph pattern; and predicting a remedial reconfiguration, wherein the remedial reconfiguration includes a reconfiguration of a design of the evaluated architecture.

Abstract: Systems and methods for building and querying an architecture knowledge graph are disclosed. In one aspect, a method includes determining an intended state and a functional state of a plurality of evaluated architectures of an evaluating organization; generating a knowledge graph, including an intended state dimension and a functional state dimension based on the determined intended state and the functional state, respectively, of the plurality of evaluated architectures; receiving, at a query engine, a natural language query; processing the natural language query with a natural language query processing engine; generating, as a result of processing the natural language query, a plurality of graph properties; querying the architecture knowledge graph using the plurality of graph properties as parameters; and displaying the results of the querying through an interface.

Abstract: Systems and methods for building a unified asset graph are disclosed. In one aspect, a method includes determining an intended state and a functional state of a plurality of evaluated architectures of an evaluating organization; generating a knowledge graph, wherein the knowledge graph includes an intended state dimension and a functional state dimension, and wherein the intended state dimension is based on the determined intended state of the plurality of evaluated architectures and the functional state dimension is based on the determined functional state of the plurality of evaluated architectures; and generating a social knowledge dimension of the knowledge graph, wherein the social dimension is based on one or more architecture nodes included in the knowledge graph, and wherein values of attribute nodes of the social knowledge dimension are at least partially based on user-generated data received by the knowledge graph.

Abstract: A method (and system) of discovering a significant subset in a collection of documents, includes identifying a set of documents from a plurality of documents based on a likelihood that documents in the set of documents carries an instance of information that is characteristic to the documents in the set of documents.

Abstract: A method (and system) for extracting information from a document, includes segregating a set of documents from a plurality of documents based on a likelihood that at least one document in the set of documents carries an instance of a preset information.

Abstract: A method (and system) of discovering a significant subset in a collection of documents, includes identifying a set of documents from a plurality of documents based on a likelihood that documents in the set of documents carries an instance of information that is characteristic to the documents in the set of documents.

Abstract: A method (and system) of discovering a significant subset in a collection of documents, includes identifying a set of documents from a plurality of documents based on a likelihood that documents in the set of documents carries an instance of information that is characteristic to the documents in the set of documents.

Abstract: A method (and system) of discovering a significant subset in a collection of documents, includes identifying a set of documents from a plurality of documents based on a likelihood that documents in the set of documents carries an instance of information that is characteristic to the documents in the set of documents.

Abstract: A method (and system) for extracting information from a document, includes segregating a set of documents from a plurality of documents based on a likelihood that at least one document in the set of documents carries an instance of a preset information.