Hewlett Packard Enterprise Development LP

Hewlett-packard Development Company, L.p.

Entit Software LLC

Halldor Janetzko

Ming C Hao

Daniel Keim

Umeshwar Dayal

Meichun Hsu

Michael Hund

Sebastian Mittelstaedt

Manish Marwah

Nelson L Chang

Wei-Nchih Lee

Martha Lurie Lyons

Soma Sundaram Santhiveeran

Walter Hill

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.

Displaying visual analytics of entity data

A pattern of geocoded pixels is generated by accessing data point values, where each data point value includes an attribute value and coordinates of a geographic location. Each data point value corresponds to a geocoded pixel that is positioned on the pattern based on the coordinates of the data point value such some geocoded pixels overlap other geocoded pixels. Different levels of the pattern of geocoded pixels correspond to a different degree of overlap between the geocoded pixels. The different levels of the pattern of geocoded pixels are associated with different magnification levels of a geographic map such that changing a magnification level of the geographic map causes a degree of overlap between the geocoded pixels of the pattern to change.

Map with different degrees of overlapping geocoded pixels

Color cell-based data placement systems, methods, and computer-readable storage media that visualize large amounts of multidimensional data on an output by rearranging data objects to variably grant more output space to areas with high data density and less output space to areas with low data density, and to variably rearrange overlapping data objects based on a number of data objects already placed at a preferred cell position for the data object.

Generalized scatter plots

Example embodiments relate to providing visual analytics of spatial time series data. In example embodiments, sensors may be located at regions within a building for collecting sensor data at regular time intervals. A sensor hierarchy can be generated including sensor nodes that are hierarchically arranged according to a physical infrastructure of the building, where each of the sensor nodes corresponds to a sensor. Sensor data can be obtained from the sensors, and a pixel calendar tree can be generated based on the sensor data and the sensor hierarchy, where the pixel calendar tree is recursively subdivided into tree portions according to a proportion of the sensor data attributable to each of the sensors. The pixel calendar tree can be displayed, where each of the tree portions includes time series sensor data of a corresponding region that is generated based on the sensor data.

Visual analytics of spatial time series data using a pixel calendar tree

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.

Visual analytics of multivariate session data using concentric rings with overlapping periods

Multivariate time-series prediction is combined with motif discovery. Multivariate time-series prediction is performed on measured (past) data points to generate or determine predicted data points. One or more motifs are discovered or determined within the combined measured data points and the predicted data points. The motifs within the measured data points are associated with the motifs within the predicted data points to permit inferences of the motifs within the measured data points to be applied to the motifs within the predicted data points. The measured data points, the predicted data points, and the motifs are displayed. User interaction with this display of the measured data points, the predicted data points, and the motifs is permitted.

Combining multivariate time-series prediction with motif discovery

A scatter plot that represents plural periodic time intervals is animated as new data points are received, where the animating includes performing real-time backward rewriting. The real-time backward rewriting includes overlaying a subset of previously written data points with the new data points, and painting a remainder of previously written data points outside the subset in the scatter plot, where painting the remainder of previously written data points is performed without shifting pixels corresponding to the remainder. A divider structure is drawn in the scatter plot to indicate a position in the scatter plot between a current time point and a previous time point.

Growing community of inventors

Konstanz, Germany

Halldor Janetzko