Capturing ordinal historical dependence in graphical event models with tree representations

A computer program product comprising:

• a computer readable storage device;
• and
• program code embodied with the computer readable storage device, the program code executable by a processor to:
• model temporal relationships between one or more events in received temporal event data, wherein each event is represented by a node;
• learn an ordered graphical event model (OGEM) graph, including capture ordinal historical dependence of the one or more events for a time window prior to each occurrence of the event, wherein the OGEM graph is a structure configured to illustrate influence of history of occurrences on rates of arrival of event types;
• automatically learn a parameter sharing architecture, including leverage the OGEM graph and learning order dependent statistical and causal co-occurrence relationships among event types;
• compute, based on a tracked history of event occurrences in real time, a control action for a corresponding predictive event type;
• generate, selectively, a control signal based on the computed control action;
• apply the generated control signal as a feedback signal to selectively control a corresponding event injection;
• dynamically issue the control signal to an operatively coupled event device associated with at least one event type reflected in the learned parameter sharing architecture, wherein the control signal configured to selectively control the event injection in accordance with the captured ordinal historical dependence, and wherein the event injection is configured to manage event behavior within the temporal event data,
• wherein the learned parameter sharing architecture is configured to capture event dynamics by one or more conditional intensity functions to measure a rate of occurrence of an event label.

A method comprising:

• modeling temporal relationships between one or more events in received temporal event data, wherein each event is represented by a node;
• learning an ordered graphical event model (OGEM) graph, including capturing ordinal historical dependence of the one or more events for a time window prior to each occurrence of the event, wherein the OGEM graph is a structure configured to illustrate influence of history of occurrences on rates of arrival of event types;
• automatically learning a parameter sharing architecture, including leveraging the OGEM graph and learning order dependent statistical and causal co-occurrence relationships among event types;
• computing, based on a tracked history of event occurrences in real time, a control action for a corresponding predictive event type;
• generating, selectively, a control signal based on the computed control action;
• applying the generated control signal as a feedback signal to selectively control a corresponding event injection;
• dynamically issuing the control signal to an operatively coupled event device associated with at least one event type reflected in the learned parameter sharing architecture, and based on the history of occurrence rates, wherein the control signal configured to selectively control the event injection in accordance with the captured ordinal historical dependence, and wherein the event injection is configured to manage event behavior within the temporal event data,
• wherein the learned parameter sharing architecture is configured to capture event dynamics by one or more conditional intensity functions for measuring a rate of occurrence of an event label.