Download - Big Data in Test and Evaluation Symposium...–Middle East Course near mile marker 9.4 ... Unlocking & providing ATEC / RDECOM data to the community is necessary, as well as continuing

1Deputy Under Secretary of the Army Test and Evaluation Office

Big Datain

Test and Evaluation

Prepared for ITEA Annual Symposium

4 October 2016

David Jimenez Director, Army Test and Evaluation


Big PictureThe tests we conducted over the past 20 years are not

representative of the next 20 years

Hypersonics

Artificial Intelligence

Cognitive Workload

Contested Information

Environments

Technology Parity

Virtual Environments

Autonomy

Cryptology

Requires Continued Investment in Infrastructure & People

Future T&E

Big Data


A Big Data Perspective

Small Data Sets

Manual Observation

Little Insights

The Good Old Days

Small

Data

Small Analysis

The Challenge

Big

Data

How do IDetermine ?

?


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 525-3-1 “The U.S. Army Operating Concept (AOC): Win in a Complex World”

found at: http://www.arcic.army.mil/Concepts/operating.aspx CEMA – Cyber Electromagnetic Activities

http://www.arcic.army.mil/Concepts/operating.aspx


Big Data CausingAn Evolution in T&E

Yesterday Today

Discrete data sets (usually associated with a single test); small overall file size

Large data sets collected over a test program (may include data from contractor tests, simulators, hardware/software-in-the-looplaboratories, M&S, fielded system, and similar systems)

Meaning derived from expert observations Meaning derived from continuous observation

Workforce has expertise in the system under test

Workforce has expertise in analytics

Evaluation products consumed by small, specialized audience

Evaluation products consumed by broad audience with diverse interests

Central evaluation question: “Did it meet requirements?”

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.


LeverageAdvances in

InstrumentationCapabilities

T&E Big Data Challenges

T&EBig Data

Free and shared among responsible practitioners

Amounts of data straining analytical resources

Support model validations

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


Big Data Changed Everything

Implications for Army Operating Concept and Force 2025: The Force 2025 Soldier will not have known a world without analytics.Our Surroundings

We expect to be able to access analytics – instantly and on demand -- to measure and understand our complex world.

Our neighbors

Our interests

Our health

Our wealth


2025 T&E and Big Data GoalsGoals:• 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.

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

Common Core Requirements:

2025 T&E

Leveraging Historical Data

Faster, More Sophisticated

Analytical Tools

Modeling & Simulation

Design of Experiments

Cloud Computing


Data Driven Deep Dive Analysis

Incident Overview– Middle East Course near mile marker 9.4– 1L and 1R (Front, Left & Right) Half-shafts broken

During that test week, vehicle completed 4 passes of this section of Middle East– 1 pass on June 5 (date incident occurred)– 3 passes on June 6

Large spike in left front spindle, frame, and driver acceleration occurred approximately 10s prior to vehicle stopping on course due to incident.

Sheared Right Side Half Shaft Sheared spline inside Left Hub Cartridge


Considerations for Big Data Analytics

Pros: Cons:Available data may be underutilized due to awareness gaps.

• What capabilities already exist?• What lessons have already been learned? • What opportunities exist?

Utilizing big data requires careful planning:• Information system and data management design • Data Collection, Reduction, Analysis (DCRA)• Archiving and sustainment

Utilizing big data requires appropriate tools.• Even small data sets are unmanageable without right tools• Tool development requires planning, time, and resources

“I paid for all this data. What can I do with it?”

Awareness

Planning

Tools


The Big Data Community

Field

T&E

User Needs

Materiel Development

S&T

Big Data

(6.1/6.2/6.3)

“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

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?


Performance Test DataIntegrated Concept Study *

PURPOSE. Address Army’s need for timely access to T&E data while aligning Army’s storage

infrastructure and protocols with DODI 5000.02.

SCOPE.

Conduct a cost-benefit analysis to determine breath & depth of data to be stored & resources

Evaluate sensitivity of results to assumption changes and identify risks associated with changes

RESPONSIBILITES.

AMSAA will appoint a Study Director

Study Advisory Group (SAG) will oversee the planning & conduct of the study

SAG Composition: Senior Executive / General Officer from: ASA(ALT) DUSA-TE, CIO/G-6,

DCS, G-3/5/7, AMC *RDECOM, ARL, & AMSAA), TRADOC CAA, & DTIC

* HQDA (DCS, G-3/5/7) Memo, subject: Performance Test Data Integrated Concept Guidance and Directive Study, 26 Feb 16


Value of ‘Deep’ Knowledge

EXAMPLE

Bad Event

IncreasedSurvivability

Analysis byService OTAs

& Others


Big Data Analysis Approach

Week’s worth of test data (~100 GB) processed within 2-3 days

1) Download vehicle data files

2) Process data for each week of test

3) Review report for reliability highlights

Run Course Identification ScriptsGPS coordinates used to ID course

Generates summary file containing metadata for each file (Vendor,

Vehicle ID, Course, Date, Miles, &

Hours

Run Data Collector ScriptsCombine files from similar vehicle, course and date

Generates files with concatenated channel data and flags the files

containing incomplete data

Run Report Generator1) Displays summary of mileage and hours2) Compares accelerations, temperatures, and speeds, across multiple vehicles3) Displays plots of major channels for each unique vehicle, course, and date combination

Generates .pdfreport


Analysis in Depth – Data Dependent

Creates context for analysis.

Multiple views of instrumentation

data channel values.

Links discrete, continuous, hierarchical,

and geospatial data types to scenario

timeline.

SASC Bill for FY17 NDAA Section 853. Enhanced use of data to improve acquisition program outcomes.

Army has been investing on the SASC’s position from the analysis and test community.

By FY18 Army T&E and Analytical communities should be up and running with a coherent data analytics and POA&M that gets at the proposed FY17 NDAA.

Unlocking & providing ATEC / RDECOM data to the community is necessary, as well ascontinuing RDT&E into tools to analyze, HPC investments, and visualization aids.


Leveraging the Big Data Space:

Use Historical Data to Right-size Future Test

Big Data

Field

T&E

User Needs


S&T(6.1/6.2/6.3)

Risk Areas = Priority Test Areas

ATEC and AMSAA analyzed 18 million milesof 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.

+

Field Test

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



Developing Cybersecurity Metrics

Big Data

Field

T&E

User Needs


S&T(6.1/6.2/6.3)

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.

0.1% Person is untrustworthy

Resource worth $1000

Threat Model for Untrustworthy Insiders



Improving System Survivability

Big Data

Field

T&E

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


New Data Scientists & Data Analysts

Visual Information

-1084-

IT Management

-2210-

Computer Engineering

-0854-

Mathematical Statistics

-1529-

Expertise in engineering; expertise in data systems, data structures, data mining and programming languages.

Expertise in scientific inquiry into complex relationships and processes using multi-disciplinary analysis tools and techniques – particularly modeling and simulation.

Expertise in statistical tools

and techniques; expertise

in applied mathematics.

Expertise in applying visual design principles to communicate complex information to diverse audiences.

Expertise in data architectures, information systems, and data management.

Computer Science-1550-

Operations Research

-1515-Expertise in high-speed computing systems, data acquisition systems, algorithm analysis and development, and information processing display, control and transfer.

WANTED: Cadre of Data Scientists and Data Analysts


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.