Integrated Data-Focus Information Environment
13 November 2013
Fleet C2 Capabilities
Bobby Junker
bobby.junker@navy.mil
Head, ONR C4ISR Department
Operational Imperative - Shortening the Kill Chain
Seamless, Transparent, Integrated, Data Centric, Agile
‘Kill’ Chain
(1) Reduce Uncertainty
Find Fix
Track Target Engage Assess
‘ Watch’ Chain
MDT
MDA
Intel
Surveil
Trk/Char/ID
Recon
Assess
Engage
Assess
Maintain High OPTEMP (2)
(3) Reduce Manpower
2
2
Legacy Information-Based Warfare
3
A2AD Operational Imperatives
Dynamic/Optimal Force Integration through seamless, automated, mission
prioritized Combat, C2 & ISR Machine-to-Machine data distribution in D-DIL Comms
4
Future Information-Based Warfare
5
Integrated C2, CS, and ISR Construct
In A2/AD D-DIL Environment
Combat
Systems (CS)
Network
Includes UxV
Common
Control
Services
 Configurable
Mediation
 Adaptable Rules
Engine
Includes:
• Force Discovery
Service
• Maestro
• IM Services
Universal
Gateway
 Adaptable Rules
Engine
 Configurable Mediation
 RTI Router
C2/ISR LAN/ACS
Systems
UxV
Control
FFDS
information
services
C2RPC
Goal: Transparency of data and
across
disparate enclaves in a D-DIL environment to support Force-Level
A2AD integrated UxV planning and execution
Data Exchange Goals
1. Increased automation of and reduced
timelines for plan-act-assess-replan cycle
– Maximize information transparency across
Force while maintaining appropriate level of
information assurance
– Minimize application design complexity by
defining interoperable middleware services in
each domain
Combat
System
CS Gateway
 Bulk (B) Data
C2 Gateway
C2 Core Services
C2 Information
Management
3. A2AD Enhancements
– Maximize effectiveness of Disconnected
Intermittent Low Bandwidth (DIL) networks
– Mission based Information Prioritization
 Messaging (M)
Data:
 tracks
 readiness
2. Federated Force Discovery Service
– Automated Force Composition &
Synchronization
– On demand Federation Across the Force
– Information Support to Applications
Two-Way
CS/C2 Data
Exchange
C2 Systems and
Applications
 ATO
 data base
updates
 readiness
 Etc.
 Video Data (V)
Distributed Tactical Cloud Construct
IC Data
Center
CORE
NODES
MOC
CVN
TOC
ONI
DoD/Svcs
RSC/COCOM
EDGE
NODES
EDGE: Ultra-thin clients, band-width
informed, greater mobility, better data
management
LCC
LHA
Tactical Cloud Construct and Issues
Overall Naval Cloud
SYSCOMs/PEOs/ONR
Naval Tactical Cloud
System Engineering
PEOs/PoRs
Naval
Tactical Cloud
Widgets & Apps
LEVERAGE
 C2RPC
 Magic Mirror
 USMC Advanced Analytics
Commodity Purchases
Naval
Tactical Cloud
Infrastructure
Naval
Tactical Cloud
Platform
Naval
Tactical Cloud
Data Science
Naval
Tactical Cloud
Analytics
Naval
Tactical Cloud
Experimentation
IaaS
LEVERAGE
 ISR-LITE Cloud Racks
 DCGS-A Tactical Edge Nodes
Data
Storage
Naval
Cloud
Platform
Utility
Virtualization
Ships
DaaS
PaaS
LEVERAGE
 IC Gov Cloud
 ACS
 FFDS
DaaS
COCs
Naval Tactical Cloud
Mgt. Policies
LEVERAGE
 ISR-LITE UOM
 Army UCD
 IC GEM
Cloud Mgt.
Tools
FFC/MARFORCOM
FLTCYBERCOM
MARFORCYBER
9
Tactical Cloud Technical Issues
• Maintaining information consistency in dynamic, D-DIL
comms environment
• Optimization of available band-width to the highest
priority information
• Distributed, dynamic Identity and Authentication
Management
• Software and data security in cloud environment
• Appropriate scaling across physical platforms
• Data normalization across large heterogeneous data
types
• Automated information prioritization
• Real-time / Near real-time operations
• …
10
The Naval C2 Big Data Challenge
OBJECTIVE: Optimize the Naval Force
vs. a Near-Peer Adversary
Force
Commander
Group
Commanders
Platform
Commanders
...
C2
Log
Optimize multi-mission
Naval Platforms/Sensors
across the Force
Force
ISR
METOC
Cyber
Group
Red
Unit
• Reduce Uncertainty
• Maintain Op Tempo
• Reduce Man Power
Blue
White
C2 Support Systems
Green
C2 Decision Makers
Historical
Data
Current
Data
Predictive
(Future)
Data
NAVAL DATA SPACE
10/30/2013 - v0.11
Bring in all possible Data to support
non-expert, C2 Decision Makers
• Leverage Historical/Forensic data to bring
expertise to the “layman”
• Leverage Future/Predictive data to bring
enhanced understanding to the “layman”
11
Leveraging the Complete
Naval “Data Space”
Naval
“Data Space”
Extend to Force Level
Expand Naval “Data
Space” with NTC
• More powerful computation
enables greater span of C2
optimization
Force
• Extend from today’s Unit level
optimization to Group and
Force level optimization
Group
Scope of Naval
“Data Space”
as it is today
Unit
Extend to Predictive Data
Extend to Historical Data
• Enhanced storage enables
much greater data storage
afloat
• More powerful analytics enable
generation of Predictive
(Future) data
• Extend from today’s Current
data set to store Historical
data sets afloat
• Extend from today’s Current
data to store Predictive data
sets afloat
Historical
Data
10/30/2013 - v0.11
Current
Data
Predictive (Future)
Data
12
Operations
• How often will batches be processed?
• How will new data types be accommodated?
– Will adding a new tagging scheme and new taskings require
recertification?
• How will ad hoc queries be handled?
– Can the inefficiency of HIVE et. al. be overcome?
• What are the personnel implications?
– Will new skills be needed
– Will more SCI clearances be needed?
Role of Forensic Data in C2
10/09/2013 - v0.14
14
Naval Problem Characterization
Naval
Planning
Volume of Data
RTRG
Naval
Patterns of Life
/Forensics
Naval
Predictive
Analysis/
Forecasting
Naval
Situational
Awareness
Naval
Effects
Naval
Target ID/
Classification
Naval
Readiness
Increasing Level of Data Science Design Difficulty
Small
Few
10/30/2013 - v0.11
Increasing Level of Computational Resources
Huge
Entity Types/Entity Complexity
Many
15
Architecture
• What are the Big Data?
• What are the appropriate processes?
– Should some data be processed before ingest?
– Should structured data be kept in a RDBMS?
– What processes can be parallelizable?
• Does cloud technology scale down?
– How many real nodes can be / need to be furnish?
• Do we need shared memory?
• Computational power appropriate for each platform or
node?
Naval Data Science Challenges
• Scoping Challenges:




What is the data that is important to Naval Operations?
What metadata do we use to describe Naval Data?
What are the entities that are important to Naval Operations?
What are the relationships that need to be established?
– Between Entities and the Data?
– Between Entities?
• Alignment Challenges:
 How do we get the Naval Community to adopt common Domain Models (e.g.,
metadata types, entity types, and relationship types)?
 How do we develop broadly useful indexing strategies across all domains?
 How do we ensure that the Naval Community’s Data Science Approach is
interoperable with other Communities (e.g., IC, Army, Air Force, . . .)
10/30/2013 - v0.11
17
Data Science Framework
• A Data Science Framework defines how metadata, entities, and
relationships are structured within the cloud platform
• A Data Science Framework consists of the patterns and
constraints that are placed on how metadata, entities, and
relationships are stored and used
• The major elements of a Data Science Framework are:
 The nouns that are used to define metadata and entities
 The verbs that are used to define relationships
 The indexing strategies
• The design of the Data Science Framework is critical because it
has a significant effect on:




10/30/2013 - v0.11
How hard/easy it is to ingest data into the cloud
How hard/easy it is to create the desired metadata, entities, and relationships
How hard/easy it is to write analytics
How hard/easy it is for applications to interact with the metadata, entities, and
relationships
18
Data Science Methodology
Operational
SME
Data
Scientist
Computer
Scientist
10/30/2013 - v0.11
Operational
Use Cases
What are the operational use cases
that you want to address?
Analytic
Capabilities
What are the analytic capabilities
that need to be developed to
support the use case?
Entity
Models
What are the entities that you need to
define to support your analytic
questions?
Analytic
Development
What analytics algorithms are required
to extract the information needed to
provide the analytic capabilities?
Data Sources
and Ingest
What data sources need to be ingested
and how do they need to be indexed?
Computer Science
Implementation
How is everything to be implemented
on top of the cloud software platform?
19
Scoping Use Cases for Experimentation
What are the operational use
cases that you want to
investigate?
Operational
Use Cases
Fleet &
Marine Corps
Need to define Use Case Slices
What are the
analytic questions
through
the Naval Big Data
that need to be answered to
Problem
support the use
case? Space to develop
Analytical
Questions
standards, patterns and practices
What are the object models that
will best allow us to answer the
analytical questions?
 Object Modeling Frameworks
 Object Model definition
 Object Model services
Analytic
Development
What analytics are required to
extract the information needed
by the use case?
 In-line NRT analytic jobs
 Batch analytic jobs
Data
Ingestion
What data sources need to be
injested to address the analytic
questions?
 Data ingest scripts
 Data visibility tagging
 Data labeling
D-DIL/A2AD
NTC Platform
What software services are
required to make everything
work and how do integrate the
Cloud with the CS?
 Data Ingest services
 Data Query and Pub/Sub services
 Analytic Services
 Cloud Security
 CS-C2 data exchange (HAG)
SLICE
Object
Models
Data
Scientists
Computer
Scientists
12/14/2012 - v0.01
20
Example of an Operational Scenario
Data Analytics performed across Warfare Areas
IAMD
ASW
• Data-driven decision guides shaped by
commander’s intent, historical decisions/results,
and COA/ECOA input across all available data
EXW
• Collaborative CS/C2/ISR/Environmental
information through data exposure and
advanced analytics (including cross-domain)
• Adaptive fleet-wide data sharing in a non-DIL and
DIL environment
Cloud Data Services
Data Cloud
Enhanced
ASW Analytics
Tags
Acoustic
Data
Data Cloud
Enhanced
IAMD Analytics
Data Cloud
Enhanced
EXW Analytics
Tags
Tags
Tags
Tags
Tags
Non
Acoustic
Blue ASW
Combat
Systems
Blue
ASW
Platforms
Blue
ASW
Sensors
Blue
ASW
Weapons
Tags
Tags
Tags
Tags
Tags
SIGINT
Data
Enemy
Sub OOB
Data
Enemy
Sub Perf.
Data
Comm.
Shipping
Data
Ocean
Data
….
• Autonomous predictive SA across warfare
domains
• Automated consolidation of UxV capabilities/
status, to align/de-conflict tasking
• Automated data security tagging at ingest
12/14/2012 - v0.01
SIGINT
METOC Data
Message Traffic
INT SUMs
SIGINT
Tracks
METOC
Radar Data
Ocean Data
Submarine Intel
MAD Data
Acoustic Data
Data Ingest
21
Operational Imperative - Shortening the Kill Chain
Seamless, Transparent, Integrated, Data Centric, Agile
‘Kill’ Chain
(1) Reduce Uncertainty
Find Fix
Track Target Engage Assess
‘ Watch’ Chain
MDT
MDA
Intel
Surveil
Trk/Char/ID
Recon
Assess
Engage
Assess
Maintain High OPTEMP (2)
(3) Reduce Manpower
•
Consistent Cloud enabled SA/C2/Execution tools & CS/C2/ISR
integrated data set across the Force
• Tactical Cloud enabled rapid/smooth integration with legacy feeds,
processes, and uses both kinetic and non-kinetic in D-DIL environment
• Cloud enabled rapid force composition changes to meet dynamic planning
and execution for success
22 simultaneously across all A2AD missions
• Seamless, fully integrated Fires across the Joint Force
22
Summary
• There are major S&T, Acquisition, and Policy challenges associated
with bringing Cloud Computing capabilities to the Fleet, particularly
for A2AD conditions
• ONR has developed a plan for systematically addressing the
challenges via its Limited Technology Experiments (LTE) process
that will result in:





Naval Tactical Cloud Reference Implementation
Naval Tactical Cloud Architecture and Design Guidance
Naval Tactical Cloud Data Models and Ingested Data Sources
Preliminary Naval Tactical Cloud Analytics, Widgets, and Apps
The framework supports Combat System, C2, logistics, personnel, ISR
(DCGS-N/MC), etc data structures in context of providing integrated
warfighting decision (C2) support
• The proposed effort could significantly accelerate the transition of
cloud technology to the Fleet
 Reduce acquisition timelines and risk
 Inform manpower planning and training development
 Facilitate co-evolution of cloud technology related CONOPs/governance