Abstract: Systems and methods of timeline visualization that define a table structure which represents supply chain management actions in a generic way. The content of the table is then processed by the timeline visualization to extract the entities, events, their relationships, and their attributes. These entities are transformed into time axes, while the events are transformed into a visual representation of their attributes using color, shapes, and text labels. The events may be positioned on a canvas, above or below a timeline axis, depending on whether they are upstream or downstream events, using a layout module which determines the position on the x-axis (time) and y-axis.

Abstract: Systems and methods that provide visualization of networks. Data is input into a table structure that represents any hierarchy of entities, relationships and their attributes. The content of the table is processed to extract the entities, relationships and their attributes. These are turned into nodes, edges and a visual representation of their attributes using color gradients, categorical colors, shapes, thickness, text labels, etc.

Abstract: Machine learning segmentation methods and systems that perform segmentation quickly, efficiently, cheaply, and optionally provides an interactive feature that allows a user to alter the segmentation until a desired result is obtained. The automated machine learning segmentation tool receives all potentially important attributes and provides segmentation of items. It also receives information about important features of the data and finds how best to differentiate between groups using cluster-based machine learning algorithms. In addition, visualization of the segmentation explains to a user how the segmentation was obtained.

Abstract: Systems and methods that provide visualization of networks. Data is input into a table structure that represents any hierarchy of entities, relationships and their attributes. The content of the table is processed to extract the entities, relationships and their attributes. These are turned into nodes, edges and a visual representation of their attributes using color gradients, categorical colors, shapes, thickness, text labels, etc.

Abstract: Machine learning segmentation methods and systems that perform segmentation quickly, efficiently, cheaply, and optionally provides an interactive feature that allows a user to alter the segmentation until a desired result is obtained. The automated machine learning segmentation tool receives all potentially important attributes and provides segmentation of items. It also receives information about important features of the data and finds how best to differentiate between groups using cluster-based machine learning algorithms. In addition, visualization of the segmentation explains to a user how the segmentation was obtained.

Abstract: Machine learning segmentation methods and systems that perform segmentation quickly, efficiently, cheaply, and optionally provides an interactive feature that allows a user to alter the segmentation until a desired result is obtained. The automated machine learning segmentation tool receives all potentially important attributes and provides segmentation of items. It also receives information about important features of the data and finds how best to differentiate between groups using cluster-based machine learning algorithms. In addition, visualization of the segmentation explains to a user how the segmentation was obtained.

Abstract: Systems and methods that provide visualization of networks. Data is input into a table structure that represents any hierarchy of entities, relationships and their attributes. The content of the table is processed to extract the entities, relationships and their attributes. These are turned into nodes, edges and a visual representation of their attributes using color gradients, categorical colors, shapes, thickness, text labels, etc.

Abstract: Systems and methods of timeline visualization that define a table structure which represents supply chain management actions in a generic way. The content of the table is then processed by the timeline visualization to extract the entities, events, their relationships, and their attributes. These entities are transformed into time axes, while the events are transformed into a visual representation of their attributes using color, shapes, and text labels. The events may be positioned on a canvas, above or below a timeline axis, depending on whether they are upstream or downstream events, using a layout module which determines the position on the x-axis (time) and y-axis.

Abstract: Systems and methods that provide visualization of networks. Data is input into a table structure that represents any hierarchy of entities, relationships and their attributes. The content of the table is processed to extract the entities, relationships and their attributes. These are turned into nodes, edges and a visual representation of their attributes using color gradients, categorical colors, shapes, thickness, text labels, etc.

Abstract: Machine learning segmentation methods and systems that perform segmentation quickly, efficiently, cheaply, and optionally provides an interactive feature that allows a user to alter the segmentation until a desired result is obtained. The automated machine learning segmentation tool receives all potentially important attributes and provides segmentation of items. It also receives information about important features of the data and finds how best to differentiate between groups using cluster-based machine learning algorithms. In addition, visualization of the segmentation explains to a user how the segmentation was obtained.