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Military Simulation Big Data Background, State of the Art and Challenges
This paper firstly reviewed several large-scale military simulations producing big data (MS big data) for a variety of usages and summarized the main characteristics of result data. Then the authors looked at the technical details involving the generation, collection, processing, and analysis of MS big data. Finally, the authors identified some key challenges and proposed a framework as a basis for future work.
Type of content:
Assets
Type of asset:
Use case
Big data potential
Yes
Policy domains:
Foreign Affairs and Defence
Phase in the policy cycle:
Policy Design and Analysis
Open license availability
No
Tags:
Open Data
Strategic planning
Addresses:
| SWOT Analysis for Military Simulation Big Data Background, State of the Art and Challenges |
||
|---|---|---|
| Helpful | Harmful | |
Internal |
Strengths• Military applications are producing massive amounts of data with plenty of Intelligence, Surveillance, and Reconnaissance (ISR) sensors, and data generated by Live, Virtual, and Constructive simulations • Sometimes the simulation generates data in a period of less than 1 ms • A high-performance computing (HPC) technique is employed as a fundamental infrastructure • Big simulation data has potential value for revealing patterns, if not accurate results |
Weaknesses• High-performance simulation algorithms and software are still insufficient: Large-scale military simulation is one of the most complex distributed applications, and performance optimization is very difficult to achieve • Limited applications for military simulation • Collecting massive data from distributed large-scale simulations may consume extra resources in terms of processor or network, which is often critical for simulation performance. • Datasets must be analysed at a rate that matches the speed of data production • Requires simulation time to advance faster than real-time and sometimes the simulation generates data in a period of less than 1 ms • Data processing technology difficulty: The data formats include unstructured (e.g., simulation log file), semi-structured (e.g., scenario configuration and simulation input), and structured (e.g., database table) types. • There is no proven formula that can be used for behaviour modelling. |
External |
Opportunities• Bigger Simulation and Data: military simulation is advancing rapidly with bigger scale and higher resolution under the impetus from modern HPC system increasing the simulation data • Unified Framework Serving Both Large-Scale Simulation and Big Data: a complete platform serving both military simulation and big data is rather limited in number. There’s need for an integrated platform to access the models, applications, resources, and data via a single entrance point • MS big data needs to be generated and analysed faster than real-time when the objective is to rapidly assess a situation and enhance decision-making • Need for development of versatile and flexible tools to mine value from the data effectively: The data formats include unstructured (e.g., simulation log file), semi-structured (e.g., scenario configuration and simulation input), and structured (e.g., database table) types • Model and input data should be verified and validated. • The concept of data farming has been proposed for many years, but it is still not broadly applied. |
Threats• Data quality: simulation data is generated by computer and can be incorrect because of flawed model • There is no proven formula that can be used for behaviour modelling • Limited applications for military simulation • People often doubt the simulation result. • Techniques and systems are still limited in their ability to provide complete solutions |
