Abstract:
According to an example, in a method for displaying visual analytics of entity data, geographic locations of entities may be plotted as first pixel cells on a first region and as second pixel cells on a second region of a geographic map. A determination may be made that the first pixel cells have a higher degree of overlap with each other in the first region compared to the second pixel cells in the second region. The geographic map may be distorted to enlarge the first region and the first pixel cells may be arranged in the first region in a manner that prevents the first pixel cells from overlapping each other. A color value for each of the pixel cells may be determined from a multi-paired color map that represents two variables corresponding to the entities by color and the pixel cells may be caused to be displayed on the distorted geographic map according to the determined respective color values.
Abstract:
A multi-attribute visualization is generated that includes non-overlapped cells that represent respective items. The cells are placed in the visualization according to geographic locations associated with the items, and the cells being assigned visual indicators to represent a first attribute of the items. The cells are arranged in clusters in the visualization, where a size of a particular one of the clusters indicates a second attribute representing a number of cases associated with a corresponding one of the items. Multiple coordinated views of the cells are presented in the visualization, the multiple views corresponding to respective different time intervals.
Abstract:
Similarities between events that include a plurality of dimensions are computed, the similarities computed based on binary comparisons between the events and based on user-specified weights for the dimensions. Multidimensional scaling (MDS) values are calculated based on the computed similarities between the events. A graphical visualization is generated of a temporal plot of the events, the temporal plot comprising a first axis corresponding to time, and a second axis corresponding to the MDS values, and the temporal plot representing overlapping time slices each containing pixels representing a respective subset of the events.
Abstract:
Visually interactive identification of a cohort of similar data objects is disclosed. One example is a system including a data processor to access a plurality of data objects, each data object comprising a plurality of numerical components, where each component represents a data feature of a plurality of data features, and to identify, for each data feature, a feature distribution of the numerical components. A selector selects a sub-plurality of the data features of a query object, where a given data feature is selected if the component representing the given data feature is a peak for the feature distribution. An evaluator determines a similarity measure based on the sub-plurality of the data features. An interaction processor iteratively processes selection of a sub-plurality of the data features based on domain knowledge, and identifies, based on the similarity measures, a cohort of data objects similar to the query object.
Abstract:
A user-selected group of data points is received. Weighted distances between further data points with the user-selected group of data points are computed, the weighted distances computed based on respective weights assigned to dimensions of data points. Density-based grouping of the further data points is performed based on the computed weighted distances, the density-based grouping producing cohorts of data points. A graphical visualization is generated including pixels representing the user-selected group of data points and the cohorts of data points. The graphical visualization provides a temporal-based visualized identification of the cohorts with the user selected group of data points.
Abstract:
Visualization of a cohort for high-dimensional categorical data is disclosed. One example is a system including a display module to identify real-time selection of a query data element in an interactive visual representation of high-dimensional categorical data elements comprising a plurality of categorical components. A matrix generator generates a binary distance matrix with columns representing categorical components, and entries in a row indicative of a degree of similarity of respective categorical components of the selected query data element to a data element represented by the row, and determines a category weighting matrix by associating a weight with entries in each column of the binary distance matrix. An evaluator evaluates a weighted similarity score for a data element represented by a row of the category weighting matrix based on entries of the row. A selector iteratively and interactively selects, based on weighted similarity scores, a cohort of categorical data elements.
Abstract:
Visual analytics for multivariate session data using concentric rings with overlapping periods includes displaying an interactive graph in a display. The interactive graph includes at least a portion of multiple concentric rings where each one of at least some of the multiple concentric rings represents periodic time units. At least some of the multiple concentric rings are divided into cells where the cells represent smaller time periods than the time units. A color of each of the cells represents a value of a metric. Also, an overlapping period ring displayed with the multiple concentric rings where the overlapping period ring comprises segments that represent overlapping metrics from the cells of the concentric rings that correspond with the segments.
Abstract:
Example embodiments relate to providing visual analytics of temporal-spatial relationships. In example embodiments, power meters may be located at regions within a building for collecting power consumption data at regular intervals. The power consumption data can be recursively processed to generate a pixel calendar tree by using a power meter hierarchy to subdivide the pixel calendar tree into tree portions according to a proportion of the power consumption data attributed to each of power meter nodes, where the tree portions are arranged in the pixel calendar tree according to an importance of the proportion; generating pixel cells in the pixel calendar tree that each represent a day in the power consumption data; and generating cell borders that each surround one of the pixel cells. At this stage, a pixel calendar display of a physical infrastructure of the building that includes the pixel cells and the cell borders can be generated.
Abstract:
Visual analytics for multivariate session data using concentric rings with overlapping periods includes displaying an interactive graph in a display. The interactive graph includes at least a portion of multiple concentric rings where each one of at least some of the multiple concentric rings represents periodic time units. At least some of the multiple concentric rings are divided into cells where the cells represent smaller time periods than the time units. A color of each of the cells represents a value of a metric. Also, an overlapping period ring displayed with the multiple concentric rings where the overlapping period ring comprises segments that represent overlapping metrics from the cells of the concentric rings that correspond with the segments.
Abstract:
Using a contingency calculation based on a number of events sharing a collection of values of plural attributes, a discriminative metric is computed representing a statistical significance of the events that share the collection of values of the plural attributes. A visualization is generated that includes cells representing respective events, the visualization including a region containing a subset of the cells corresponding to the collection of values of the plural attributes, and the visualization including a significance visual indicator associated with the region to indicate the statistical significance of the events sharing the collection of values of the plural attributes.