Big Data in Army Test and Evaluation

Transcription

1 Big Data in Army Test and Evaluation Prepared for ITEA System-of-Systems Engineering Workshop 26 January 2017 COL Patrick Walden, Senior Military Advisor for Army Test and Evaluation 1

2 Army Test and Evaluation Enterprise challenges and opportunities Continue Tri-Service Cooperation & Joint Invest. Meeting New data challenges Modeling and Simulation Emerging Technology testing Spectrum Budget uncertainty Speed of informing (performance) Depth of analysis Workforce Career Path Professional Dev. Professional Outreach Hiring Authorities DoD T&E Oversight (DT and OT) T&E Governance Authorities (NDAA 2003) Support to Experimentation (informing requirements) Support to Prototyping 2

3 Force 2025 and T&E Service OTAs must determine whether or not systems are effective, suitable, and survivable in support of unified land operations in an operational environment dominated by: Increased momentum of human interaction Potential overmatch Importance of cyber and space Dense urban areas (megacities) Ubiquitous media WMD proliferation CEMA! Big Data! Increasing complexity on the battlefield increases complexity in T&E. Demand for data and the means to use it effectively is also increasing. See TRADOC PAM The U.S. Army Operating Concept (AOC): Win in a Complex World found at: CEMA Cyber Electromagnetic Activities 3

4 Big Data Changed Everything We expect to be able to access analytics instantly and on demand to measure and understand our complex world. Our wealth Our wealth Even image searches Our health Our neighbors Our Surroundings Our interests Implications for Army Operating Concept and Force 2025: The Force 2025 Soldier will not have known a world without analytics. 4

5 Big Data Has Caused An Evolution in T&E Yesterday Today Discrete data sets (usually associated with a single test); small overall file size Meaning derived from expert observations Workforce has expertise in the system under test Evaluation products consumed by small, specialized audience Central evaluation question: Did it meet requirements? Large data sets collected over a test program (may include data from contractor tests, simulators, hardware/software-in-the-loop laboratories, M&S, fielded system, and similar systems) Meaning derived from continuous observation Workforce has expertise in analytics Evaluation products consumed by broad audience with diverse interests Central evaluation question: What are the system s strengths and limitations over the range of conditions found on a complex, interoperating battlefield? To focus on the Why and How of a system s operational effectiveness, operational suitability, and survivability, increases the demand for deep analytics. 5

6 T&E Big Data Challenges Free and shared among responsible practitioners T&E Big Data Support model validations Leverage Advances in Instrumentation Capabilities Amounts of data straining analytical resources More reliance on supercomputing Need tools to make short order of analysis (visualization, sage, and frame capture) T&E Cadre of the Future Requires Data Scientists and Data Analysts 6

7 Considerations for Big Data Analytics Awareness I paid for all this data. What can I do with it? Cons: Available data may be underutilized due to awareness gaps: What capabilities already exist? What lessons have already been learned? What opportunities exist? Planning Tools Utilizing big data requires careful planning: Information system and data management design Data Collection, Reduction, Analysis (DCRA) Archiving and sustainment ( context of the event) Utilizing big data requires appropriate tools: Even small data sets are unmanageable without right tools Tool development requires planning, time, and resources 7

8 The Big Data Community Field Big Data User Needs Big Data is a common resource of the Services analytical community. Diverse analytical organizations contribute to and draw from it: Data acquisition methods Computational resources Models, simulations, laboratories, tools Historical data Expertise T&E Materiel Development S&T (6.1/6.2/6.3) Important questions going forward: Who manages it for stakeholders? Who sustains it? How do we establish business rules for increased collaboration? Can we obtain synergies through collaboration? 8

9 Value of Deep Knowledge: Example Bad Event Analysis by Service OTAs & Others Increased Survivability 9

10 Big Data Analysis Approach 1) Download vehicle data files 2) Process data for each week of test Run Course Identification Scripts GPS coordinates used to ID course Generates summary file containing metadata for each file (Vendor, Vehicle ID, Course, Date, Miles, & Hours Run Data Collector Scripts Combine files from similar vehicle, course and date Generates files with concatenated channel data and flags the files containing incomplete data Run Report Generator 1) Displays summary of mileage and hours 2) Compares accelerations, temperatures, and speeds, across multiple vehicles 3) Displays plots of major channels for each unique vehicle, course, and date combination Generates.pdf report 3) Review report for reliability highlights Week s worth of test data (~100 GB) processed within 2-3 days 10

11 Leveraging the Big Data Space: Use Historical Data to Right-size Future Test Field User Needs Field Test T&E Big Data S&T (6.1/6.2/6.3) + Materiel Development ATEC and AMSAA analyzed 18 million miles of Stryker field and T&E data to develop reliability risk areas. Insights will be used to shape test scope on future versions of the systems. Subsystem X Assembly Y Component AB Sub-assembly Z Block interface W Widget subsystem Assembly Case Sub-component ABC Nuts and bolts Assembly Main element Subsystem Box K Superstructure Link Block assembly Crankstick Beta Shaft Structure Drive Component Widget XYZ Interface Risk Areas = Priority Test Areas 11

12 Leveraging the Big Data Space: Developing Cybersecurity Metrics Field User Needs Threat Model for Untrustworthy Insiders T&E Big Data S&T (6.1/6.2/6.3) Materiel Development 0.1% Person is untrustworthy Resource worth $1000 ATEC leveraging Network Integration Evaluation (NIE) events to develop models, methodologies, and metrics for cybersecurity T&E. Insights will be used to enable earlier-in-life cycle assessments and requirements development. 12

13 Leveraging the Big Data Space: Improving System Survivability T&E Field Big Data Materiel Development User Needs S&T (6.1/6.2/6.3) ATEC combined insights about ballistic events on vehicles in theater from: - Intelligence community s trend analyses - On-board vehicle instrumentation - Ballistic response data from live fire testing. - Modeling and simulation Insights used to improve: - Current and future system survivability designs - Test Scope - Test and evaluation methodology - Instrumentation and simulation designs 13

14 Goals: 2025 T&E and Big Data Goals Utilize knowledge, information, and data to achieve core mission and business objectives. Faster, more Accurate Decision-Making Cost Optimization Quicker Responses to Requests for Information More Holistic Test and Evaluation Automated tracking items or status Make useful big data capabilities available to everyone, but tailored to specific needs. Common Core Requirements: Sustainment of data for long term use (Archival) Discoverability and Access to data Analytics of historical and current information Derive context to inform decision making 14

15 WANTED: Cadre of Data Scientists and Data Analysts Expertise in statistical tools and techniques; expertise in applied mathematics. Expertise in high-speed computing systems, data acquisition systems, algorithm analysis and development, and information processing display, control and transfer. Computer Science Mathematical Statistics IT Management Operations Research Computer Engineering Expertise in scientific inquiry into complex relationships and processes using multidisciplinary analysis tools and techniques particularly modeling and simulation. Expertise in engineering; expertise in data systems, data structures, data mining and programming languages. Expertise in data architectures, information systems, and data management. Visual Information Expertise in applying visual design principles to communicate complex information to diverse audiences. New Data Scientists & Data Analysts 15

16 Conclusions Big Data analysis : Terabytes of Data Greater Insights? High potential to leverage learn /understand behaviors of complex systems High potential of over-analysis for sake of over-analysis New generation of Data Scientists needed Real data-driven evidence to investigate anomalies - attribution Investments required: New methods and tools to quickly process and analyze Big Data Support the enterprise decision processes Develop a sharing culture DOD data policy evolutions Big Data will change our T&E enterprise in ways we don t completely grasp yet. 16