Abstract: A computer-implemented method includes receiving a first dataset including a first table having a first number of parts, where each part of the first number of parts represents either a row or a column of the first table and receiving a second dataset including a second table having a second number of parts, where each part of the second number of parts represents either a row or a column of the second table. For each part of the first number of parts the method includes forming a string representation of the part from a number of values associated with the part and determining a numerical representation of the string representation of the part. For each part of the second number of parts, the method includes forming a string representation of the part from a number of values associated with the part and determining a numerical representation of the string representation of the part.

Abstract: A computer-implemented method for automatically determining data relationships includes generating a graphical user interface (GUI) that allows a user to intuitively form a customized model of data from different data sources. The GUI includes icons that represent data sources, data variable selection, data modeling, and data prediction. The icons can be logically arranged to form a customized model without any additional user input or knowledge of data modeling. A prediction GUI allows the user to set customized weights of data variables in the model to form predictive controls for data prediction such as in what-if scenarios.

Abstract: A computer-implemented method comprising: receiving, with a computer, first and second datasets; performing, with the computer, column discovery on the first and second datasets using a first trained machine-learning model to produce a column map that indexes one or more columns in the first dataset to one or more columns in the second dataset; performing, with the computer, row discovery on the first and second datasets using a second trained machine-learning model, a trained approximate nearest neighbor index, and the column discovery to produce a row map that indexes one or more rows in the first dataset to one or more rows in the second dataset; combining, with the computer, the first and second datasets using the column map and the row map to form a combined dataset; and performing one or more actions with the combined dataset.

Abstract: A method is disclosed including: receiving a graph-based program that identifies a bipartite graph and one or more update function sets, the bipartite graph including a plurality of graph nodes and a plurality of edges, such that each graph node corresponds to one of the update function sets; associating each of a plurality of computing units with a different respective one of the graph nodes; instantiating, by a Publisher Subscriber platform, a plurality of channels, the plurality of channels defining a topology that matches a topology of the bipartite graph; and executing the graph-based program based on the plurality of channels to produce a result.

Abstract: A computer-implemented method for automatically determining data relationships includes generating a graphical user interface (GUI) that allows a user to intuitively form a customized model of data from different data sources. The GUI includes icons that represent data sources, data variable selection, data modeling, and data prediction. The icons can be logically arranged to form a customized model without any additional user input or knowledge of data modeling. A prediction GUI allows the user to set customized weights of data variables in the model to form predictive controls for data prediction such as in what-if scenarios.

Abstract: A method is disclosed including: receiving a graph-based program that identifies a bipartite graph and one or more update function sets, the bipartite graph including a plurality of graph nodes and a plurality of edges, such that each graph node corresponds to one of the update function sets; associating each of a plurality of computing units with a different respective one of the graph nodes; instantiating, by a Publisher Subscriber platform, a plurality of channels, the plurality of channels defining a topology that matches a topology of the bipartite graph; and executing the graph-based program based on the plurality of channels to produce a result.

Abstract: A method is disclosed including: receiving a graph-based program that identifies a bipartite graph and one or more update function sets, the bipartite graph including a plurality of graph nodes and a plurality of edges, such that each graph node corresponds to one of the update function sets; associating each of a plurality of computing units with a different respective one of the graph nodes; instantiating, by a Publisher Subscriber platform, a plurality of channels, the plurality of channels defining a topology that matches a topology of the bipartite graph; and executing the graph-based program based on the plurality of channels to produce a result.

Abstract: A computer-implemented method for improving compliance with a therapeutic regimen for each of a plurality of patients includes, for each patient, causing presentation by the server to the patient via an application running on the mobile computing device, on at least a daily basis, of (i) an updated list of items to be performed by the patient on the date of presentation on the updated list and (ii) a region on a display of the mobile computing device for reporting performance of items on the updated list so as to cause sending by the mobile computing device to the server of patient compliance data; and receiving at the server from the mobile computing device the patient compliance data, storing such data and using such data in calculating the updated list for presentation to the patient via the mobile computing device on a subsequent occasion.