Geoanalytics Platform for Special Operations Mission Readiness
EPIC Ready objectively measures the tasks, conditions and standards assigned to the Joint Mission Essential Tasks (JMETs) associated with a unit’s objectives. This data, which helps determine training effectiveness and mission readiness, can be easily exported to JTIMS. It improves rapid field reviews. Daily objectives reports, which can be completed in minutes, capture whether training objectives are being met and, if not, provide the instant information needed to adjust on the fly.Report. From exercise planners to unit commanders to the joint staff, leadership on all levels receives the exact information needed to determine success and, if shortfalls exist, help determine why. In addition to better information, PAS also reduces the time required for after-action reporting from months to days. Furthermore, you can create immediate lessons learned to enhance planning for the next joint exercise life cycle. Intuitive and simple to use, PAS has the flexibility needed to handle today’s changing requirements. PAS provides the unit performance data needed to ensure force readiness and increase return on investment (ROI) in training. Spanning the Joint Exercise Life Cycle.PAS helps:,Plan and run exercises,Measure resultsFine-tune training Quantify force readiness Improve ROI
IDEALM: Efficient Data Reduction with Locally Exchangeable Measures
LBNL has developed IDEALM, a dynamic sampling algorithm that reduces large streaming data, yet provides accurate information about the data for analysis. IDEALM could prove beneficial to network routers, for use in network monitoring mechanisms; facilities that generate large amounts of data, as a means to reduce data volume; and social networks, among other applications. IDEALM can be used for streaming data in high frequency as well as stored data. Large streaming data are an essential part of computational modeling and network communications. Yet such data are generally intractable to store, compute, search, and retrieve. This dynamic data reduction algorithm detects redundant patterns and reduces data size up to 100 times by exploiting the exchangeability of measurements. IDEALM exploits both redundancies of data in a time series and redundancies of data distribution. Drawbacks to today's common techniques in network monitoring and other practices to reduce the size of collected monitoring measurements -- such as storing a random sample or spectral analysis -- are impractical for large streaming data in high frequency. LBNL's IDEALM resolves issues with current approaches.