Abstract: A method comprises receiving data points from a spreadsheet, mapping the data points to a reference space, generating a cover of the reference space, clustering the data points mapped to the reference space to determine each node of a graph, each node including at least one data point, generating a visualization depicting the nodes, the visualization including an edge between every two nodes that share at least one data point, generating a translation data structure indicating location of the data points in the spreadsheet as well as membership of each node, detecting a selection of at least one node, determining the location of data points in the spreadsheet corresponding to data points that are members of the selected node(s) using the translation data structure, and providing a first command to a spreadsheet application to provide a first visual identification of the first set of data points in the spreadsheet.

Abstract: A method comprises receiving a data set, mapping data points from the data set to a reference space utilizing a lens function, generating a cover of the reference space using a resolution function, clustering the data points mapped to the reference space using the cover and a metric function to determine each node of a plurality of nodes of a graph, generating a graph including the plurality of nodes, the graph including an edge between every two nodes that share at least one data point as a member, and generating first and second data structures, the first data structure identifying membership of each node, the second data structure identifying each edge between each of the two nodes, the second data structure further identifying the nodes that are connected by each edge, the first and second data structure being capable of being queryable using a query language.

Abstract: A method comprises dividing a set of data points into a structure subset and boost subsets, adding the data points in structure subset into each boost subset, analyzing the structure subset using topological data analysis (TDA) to identify nodes of a structure graph, boost graph, and modified graph, analyze each of the boost subsets using the TDA to identify additional nodes of boost graph, for each node in each of the plurality of boost graphs that do not share at least one data point with a node in the structure graph, adding the node of a particular boost subset including data points that are members of the node, to the modified graph, and generating report indicating relationships between data points of the set of data points based on the nodes of the modified graph.

Abstract: An example method includes receiving a first set of data identifying entities and performance information for analysis, receiving a second set of data identifying entities and performance information associated with known or suspected past fraud or abuse, receiving metric and lens selections, performing metric and lens functions based on the metric and lens selections on first and second set of data, generating cover of reference space and cluster mapped performance information to identify nodes in a graph, each node including one or more entities as members, each node being connected to another node if they share at least one common entity as members, identifying nodes that include at least one member from the second set of data, determining entities that are members of the identified nodes that are from the first set of data, and generating a first report listing the determined entities as possibly involved in fraud or waste.

Abstract: A method comprises receiving a network of a plurality of nodes and a plurality of edges, each of the nodes comprising members representative of at least one subset of training data points, each of the edges connecting nodes that share at least one data point, grouping the data points into a plurality of groups, each data point being a member of at least one group, creating a first transformation data set, the first transformation data set including the training data set as well as a plurality of feature subsets associated with at least one group, values of a particular data point for a particular feature subset for a particular group being based on values of the particular data point if the particular data point is a member of the particular group, and applying a machine learning model to the first transformation data set to generate a prediction model.

Abstract: An example method comprises receiving historical information of episodes, constructing event sets from the historical information, categorizing each event with general labels and synthetic labels, learning an event metric on the events by using the general and synthetic labels to perform dimensionality reduction to associate a vector with each event and to determine an angle between every two vectors, determining an event set metric using distances between each pair of event sets, deriving a sequence metric on the episodes, the sequence metric obtaining a preferred match between two episodes, deriving a subsequence metric on the episodes, the subsequence metric is a function of the event set metric on subsequences of each episode, grouping episodes into subgroups based on distances, for at least one subgroup, generating a consensus sequence by finding a preferred sequence of events, and the episodes of the subgroup, and generating a report indicating the consensus sequence.

Abstract: Exemplary systems and methods to improve capture of relationships within information are provided. In various embodiments, a system comprises a landmark module configured to choose a set of landmarks from data in a finite metric space, the set of landmarks being a subset of points in the finite metric space, a nearest neighbor module configured to compute, for each landmark, a predetermined number of nearest neighbor landmarks in the set of landmarks, a graph construction module configured to identify at least one pair of landmarks that are nearest neighbors to each other, an edge generator module configured to add an edge between the at least one pair of landmarks, and a non-landmark projection module configured to project non-landmark points based on the landmarks and one or more edges thereby enabling at least one shape to indicate relationships in the data.

Abstract: In some embodiments, a method includes selecting a first and a second predetermined number of points of graphical data; determining an initial distance between each of the first predetermined number of points, generating a fast approximate distance oracle comprising identifiers of points and oracle distances between at least one point and another point, determining a graphical distance for each of the second predetermined number of points relative to other points of the first and second predetermined number of points, calculating a potential error between the determined graphical distance and a topologic distance between each of the second predetermined number of points, approximating a correction for at least one of the second predetermined number of points, adjusting a position of the at least one of the second predetermined number of points, and displaying the at least one of the second predetermined number of points.

Abstract: Exemplary systems and methods for visualization of data analysis are provided. In various embodiments, a method comprises accessing a database, analyzing the database to identify clusters of data, generating an interactive visualization comprising a plurality of nodes and a plurality of edges wherein a first node of the plurality of nodes represents a cluster and an edge of the plurality of edges represents an intersection of nodes of the plurality of nodes, selecting and dragging the first node in response to a user action, and reorienting the interactive visualization in response to the user action of selecting and dragging the first node.

Abstract: In various embodiments, a system comprises a map and a patient data assessment module. The map includes a plurality of groupings and interconnections of the groupings, each grouping having one or more patient members that share biological similarities, each interconnection interconnecting groupings that share at least one common patient member, the map identifying a set of groupings and a set of interconnections having a medical characteristic of a set of medical characteristics. The patient data assessment module may be configured to receive sensor data from a user's mobile device and to assess the sensor data to generate user medical attributes, to determine whether the user shares the biological similarities with the one or more patient members of each grouping based, at least in part, on the user medical attributes, thereby enabling association of the user with one or more of the set of medical characteristics.

Abstract: An exemplary method comprises receiving graph data, generating an edge draw command to draw a first and a second edge as a display object, buffering first and second node positions for first and second sprites, respectively, identifying shader information associated with application of shading of the first sprite and the second sprite, the shader information indicating a change of shade based on distance from a first position and a change of shade based on distance from a second position associated with the second sprite, generating a node draw command to draw at least the first and second sprites, generating a graph shading command to apply shading to at least a portion of the first sprite based and to apply shading to at least a portion of the second sprite based, and providing the edge draw command, node draw command, and graph shading command to a graph execution module.

Abstract: Exemplary systems and methods for visualization of data analysis are provided. In various embodiments, a method comprises accessing a database, analyzing the database to identify clusters of data, generating an interactive visualization comprising a plurality of nodes and a plurality of edges wherein a first node of the plurality of nodes represents a cluster and an edge of the plurality of edges represents an intersection of nodes of the plurality of nodes, selecting and dragging the first node in response to a user action, and reorienting the interactive visualization in response to the user action of selecting and dragging the first node.