Abstract: The various embodiments described herein include methods, systems and/or devices used to visualize data. In one aspect, a method is performed by a computing system having one or more processors and memory. The method includes (1) receiving a request from a user to visualize data, the data stored in a graph dataflow processing system, and (2) in response to the request, invoking an interactive graphical user interface (GUI) for display to the user, the GUI including a first set of visualization data corresponding to a first subset of the data.

Abstract: The subject technology trains, for a first set of iterations, a first machine learning model using a loss function with a first set of parameters. The subject technology determines, by a second machine learning model, a state of the first machine learning model corresponding to the first set of iterations. The subject technology determines, by the second machine learning model, an action for updating the loss function based on the state of the first machine learning model. The subject technology updates, by the second machine learning model, the loss function based at least in part on the action, where the updated loss function includes a second set of parameters corresponding to a change in values of the first set of parameters. The subject technology trains, for a second set of iterations, the first machine learning model using the updated loss function with the second set of parameters.

Abstract: A method of optimizing graph operations is performed by a computing system. The method comprises: (1) receiving a first request to perform a first operation on a first graph, where the first graph comprises a set of vertices and a set of edges, each edge connecting a pair of vertices, and each vertex having one or more associated properties; (2) logging the first request, but not performing the first operation; (3) receiving a second request to perform a second operation; (4) logging the second request, but not performing the second operation; (5) receiving a query for data from the first graph, where the data includes property values for one or more vertices; (6) in response to the query: (a) generating a second graph by optimizing and performing the first and second operations; and (b) returning data responsive to the query, where the returned data is based on the second graph.

Abstract: A method receives a first request from a client object at a device. The first request specifies a data source. In response to the first request, the method uploads data from the data source, stores the data as a plurality of first columns, and instantiates a first server object that provides access to the first columns. The method later receives a second request from the client object. The second request specifies a transformation of the data. In response to the second request, the method stores one or more additional columns and instantiates a second server object that provides access to the additional columns and one or more of the first columns. Each of the additional columns is constructed from the first columns according to the requested transformation, and each of the additional columns includes a plurality of data values all having the same data type.

Abstract: A method of optimizing graph operations is performed by a computing system. The method comprises: (1) receiving a first request to perform a first operation on a first graph, where the first graph comprises a set of vertices and a set of edges, each edge connecting a pair of vertices, and each vertex having one or more associated properties; (2) logging the first request, but not performing the first operation; (3) receiving a second request to perform a second operation; (4) logging the second request, but not performing the second operation; (5) receiving a query for data from the first graph, where the data includes property values for one or more vertices; (6) in response to the query: (a) generating a second graph by optimizing and performing the first and second operations; and (b) returning data responsive to the query, where the returned data is based on the second graph.

Abstract: Methods and systems for node and link detection in social network analysis. Interactive noise reduction allows reduction of the data set under analysis to enable substantially real time detection of links and nodes.

Abstract: The various embodiments described herein include methods, systems and/or devices used to visualize data. In one aspect, a method is performed by a computing system having one or more processors and memory. The method includes (1) receiving a request from a user to visualize data, the data stored in a graph dataflow processing system, and (2) in response to the request, invoking an interactive graphical user interface (GUI) for display to the user, the GUI including a first set of visualization data corresponding to a first subset of the data.

Abstract: A method of optimizing graph operations is performed by a computing system. The method comprises: (1) receiving a first request to perform a first operation on a first graph, where the first graph comprises a set of vertices and a set of edges, each edge connecting a pair of vertices, and each vertex having one or more associated properties; (2) logging the first request, but not performing the first operation; (3) receiving a second request to perform a second operation; (4) logging the second request, but not performing the second operation; (5) receiving a query for data from the first graph, where the data includes property values for one or more vertices; (6) in response to the query: (a) generating a second graph by optimizing and performing the first and second operations; and (b) returning data responsive to the query, where the returned data is based on the second graph.

Abstract: A method receives a first request from a client object at a device. The first request specifies a data source. In response to the first request, the method uploads data from the data source, stores the data as a plurality of first columns, and instantiates a first server object that provides access to the first columns. The method later receives a second request from the client object. The second request specifies a transformation of the data. In response to the second request, the method stores one or more additional columns and instantiates a second server object that provides access to the additional columns and one or more of the first columns. Each of the additional columns is constructed from the first columns according to the requested transformation, and each of the additional columns includes a plurality of data values all having the same data type.