A method of analyzing coverage, bias and model explanations in large dimensional modeling data, the method comprising: discretizing, by one or more computer processors, three or more variables of a dataset, by splitting each variable into value ranges called bins, to generate a discretized phase space represented as a grid of a plurality of cells, the dataset comprising network activity data, wherein the dataset comprises a plurality of records, each record of the plurality of records having a value and a unique identifier; applying, by the one or more computer processors, a grid transformation to each record in the dataset to assign each record to a cell of the plurality of cells of the grid according to the grid transformation; generating, by the one or more computer processors, a grid index to reference each cell using a discretized feature vector; creating, by the one or more computer processors, a grid storage for storing the records assigned to each cell of the grid using a key-value pair, wherein the value part of the key-value pair corresponds to the ID of each record used as a reference to each record, and wherein the key part of the key-value pair corresponds to the discretized feature vector used as a key to each cell, and wherein the value part of the key-value pair is implemented as a list data structure; creating, by the one or more computer processors, a coverage storage for storing coverage data representing a number of records assigned to each cell, wherein the coverage storage uses a key-value pair, and wherein the discretized feature vectors are used as the key corresponding to each cell, and wherein the value in the coverage storage is the size of the list stored in the value part of the grid data storage, and wherein the key-value storage in the grid storage and the coverage storage is an in-memory database; applying, by the one or more computer processors, compaction to the coverage storage, wherein the coverage data is aggregated from a higher dimensional grid to a grid formed by a subset of selected variables; applying, by the one or more computer processors, compaction to a selected subset of three variables; and generating, by the one or more computer processors, a trivariate heatmap that visualizes a plotted intersection of two of the three variables, and a plot for each bin of the third variable, wherein the trivariate heatmap identifies distinct patterns in the dataset, and wherein a 2-dimensional bin showing nonlinearity represents a bias or correlations in the dataset that are non-causal. discretizing, by one or more computer processors, three or more variables of a dataset, by splitting each variable into value ranges called bins, to generate a discretized phase space represented as a grid of a plurality of cells, the dataset comprising network activity data, wherein the dataset comprises a plurality of records, each record of the plurality of records having a value and a unique identifier; applying, by the one or more computer processors, a grid transformation to each record in the dataset to assign each record to a cell of the plurality of cells of the grid according to the grid transformation; generating, by the one or more computer processors, a grid index to reference each cell using a discretized feature vector; creating, by the one or more computer processors, a grid storage for storing the records assigned to each cell of the grid using a key-value pair, wherein the value part of the key-value pair corresponds to the ID of each record used as a reference to each record, and wherein the key part of the key-value pair corresponds to the discretized feature vector used as a key to each cell, and wherein the value part of the key-value pair is implemented as a list data structure; wherein the value part of the key-value pair corresponds to the ID of each record used as a reference to each record, and wherein the key part of the key-value pair corresponds to the discretized feature vector used as a key to each cell, and wherein the value part of the key-value pair is implemented as a list data structure; creating, by the one or more computer processors, a coverage storage for storing coverage data representing a number of records assigned to each cell, wherein the coverage storage uses a key-value pair, and wherein the discretized feature vectors are used as the key corresponding to each cell, and wherein the value in the coverage storage is the size of the list stored in the value part of the grid data storage, and wherein the key-value storage in the grid storage and the coverage storage is an in-memory database; wherein the coverage storage uses a key-value pair, and wherein the discretized feature vectors are used as the key corresponding to each cell, and wherein the value in the coverage storage is the size of the list stored in the value part of the grid data storage, and wherein the key-value storage in the grid storage and the coverage storage is an in-memory database; applying, by the one or more computer processors, compaction to the coverage storage, wherein the coverage data is aggregated from a higher dimensional grid to a grid formed by a subset of selected variables; applying, by the one or more computer processors, compaction to a selected subset of three variables; and generating, by the one or more computer processors, a trivariate heatmap that visualizes a plotted intersection of two of the three variables, and a plot for each bin of the third variable, wherein the trivariate heatmap identifies distinct patterns in the dataset, and wherein a 2-dimensional bin showing nonlinearity represents a bias or correlations in the dataset that are non-causal.