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Critical systems that produce big data streams can require human operators to monitor these event streams for changes of interest. Automated systems which oversee many tasks can still have a need for the ‘human-in-the-loop’ operator to evaluate whether an intervention is required due to a lack of suitable training data initially offered to the system which would allow a correct course of actions to be taken. In order for an operator to be capable of reacting to real-time events, the visual depiction of the event data must be in a form which captures essential associations and is readily understood by visual inspection. A similar requirement can be found during inspections on activity protocols in a large organization where a code of correct conduct is prescribed and there is a need to oversee whether the activity traces match the expectations, with minimal delay. The methodology presented here addresses these concerns by providing an adaptive window sizing measurement for subsetting the data, and subsequently produces a set of network diagrams based upon event label co-occurrence networks. With an intuitive method of network construction the amount of time required for operators to learn how to monitor complex event streams of big datasets can be reduced.

Adaptive network diagram constructions for representing big data event streams on monitoring dashboards