| dc.description.abstract | Successful mission-centric collaboration depends on situational awareness in an increasingly complex mission environment. To support timely and reliable high level mission decisions, auditing tools need real-time data for effective assessment and optimization of mission behaviors. In the context of a battle rhythm, mission health can be measured from workflow generated activities. Though battle rhythm collaboration is dynamic and global, a potential enabling technology for workflow behavior auditing exists in process mining.
However, process mining is not adequate to provide mission situational awareness in the battle rhythm environment since event logs may contain dynamic mission states, noise and timestamp inaccuracy. Therefore, we address a few key near-term issues. In sequences of activities parsed from network traffic streams, we identify mission state changes in the workflow shift detection algorithm. In segments of unstructured event logs that contain both noise and relevant workflow data, we extract and rank workflow instances for the process analyst. When confronted with timestamp inaccuracy in event logs from semi automated, distributed workflows, we develop the flower chain network and discovery algorithm to improve behavioral conformance. For long term adoption of process mining in mission centric collaboration, we develop and demonstrate an experimental framework for logging uncertainty testing. We show that it is highly feasible to employ process mining techniques in environments with dynamic mission states and logging uncertainty.
Future workflow behavior auditing technology will benefit from continued algorithmic development, new data sources and system prototypes to propel next generation mission situational awareness, giving commanders new tools to assess and optimize workflows, computer systems and missions in the battle space environment. | en |
