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Integrated Data-Focus Information Environment

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 . MDA. MDT. Find. Fix. Track. Target. Engage. Assess. ‘ Watch’ Chain. Intel.

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Integrated Data-Focus Information Environment

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  1. Integrated Data-Focus Information Environment 13 November 2013 Fleet C2 Capabilities • Bobby Junker • bobby.junker@navy.mil • Head, ONR C4ISR Department

  2. Operational Imperative - Shortening the Kill Chain MDA MDT Find Fix Track Target Engage Assess ‘ Watch’ Chain Intel Surveil Trk/Char/ID Recon Assess Engage Assess Seamless, Transparent, Integrated, Data Centric, Agile ‘Kill’ Chain (1) Reduce Uncertainty Time Sensitive Targeting (TST) Maintain High OPTEMP (2) (3) Reduce Manpower 2

  3. Legacy Information-Based Warfare

  4. A2AD Operational Imperatives Dynamic/Optimal Force Integration through seamless, automated, mission prioritized Combat, C2 & ISR Machine-to-Machine data distribution in D-DIL Comms

  5. Future Information-Based Warfare

  6. 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 C2RPC • Goal: Transparency of data and information services across disparate enclaves in a D-DIL environment to support Force-Level A2AD integrated UxV planning and execution FFDS

  7. Data Exchange Goals 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 Federated Force Discovery Service Automated Force Composition & Synchronization On demand Federation Across the Force Information Support to Applications A2AD Enhancements Maximize effectiveness of Disconnected Intermittent Low Bandwidth (DIL) networks Mission based Information Prioritization • Two-Way CS/C2 Data Exchange • Messaging (M) Data: • tracks • readiness • Bulk (B) Data • ATO • data base updates • readiness • Etc. • Video Data (V)

  8. Distributed Tactical Cloud Construct IC Data Center CORE NODES MOC CVN TOC ONI LHA LCC DoD/Svcs RSC/COCOM EDGE NODES EDGE: Ultra-thin clients, band-width informed, greater mobility, better data management

  9. SYSCOMs / PEOs / ONR PEOs / PoRs Naval Tactical Cloud System Engineering Naval s A e r s c a h Tactical Cloud C e s Widgets & Apps U Commodity Purchases Naval Naval Naval Tactical Cloud Tactical Cloud Tactical Cloud Platform Data Science Experimentation e y g a t i a t Ships Naval l a r i t o D t Cloud U S Platform PaaS COCs Naval Tactical Cloud Mgt . Policies . t g S M e c a t u a M r i D t g t y . Cloud Mgt . Tools g R n e i s n o o i u s r i c v e o r M P g t . Tactical Cloud Construct and Issues Overall Naval Cloud LEVERAGE i t e · c C 2 RPC t u r e · Magic Mirror · USMC Advanced Analytics e c n a m D r e o s f i r g e n P Naval Naval Tactical Cloud Tactical Cloud Infrastructure Analytics Virtualization DaaS IaaS DaaS LEVERAGE LEVERAGE LEVERAGE · · ISR - LITE Cloud Racks IC Gov Cloud · ISR - LITE UOM · · DCGS - A Tactical Edge Nodes ACS · Army UCD · FFDS · IC GEM FFC / MARFORCOM FLTCYBERCOM MARFORCYBER

  10. 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 …

  11. The Naval C2 Big Data Challenge C2 Decision Makers OBJECTIVE: Optimize the Naval Force vs. a Near-Peer Adversary ForceCommander GroupCommanders PlatformCommanders C2 Support Systems . . . C2 Log Optimize multi-mission Naval Platforms/Sensors across the Force NAVAL DATA SPACE ISR METOC Cyber • Reduce Uncertainty • Maintain Op Tempo • Reduce Man Power 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”

  12. Leveraging the CompleteNaval “Data Space” Naval “Data Space” Expand Naval “Data Space” with NTC • Extend to Force Level • More powerful computation enables greater span of C2 optimization • Extend from today’s Unit level optimization to Group and Force level optimization Force Group Scope of Naval “Data Space”as it is today Unit • Extend to Historical Data • Enhanced storage enables much greater data storage afloat • Extend from today’s Current data set to store Historical data sets afloat • Extend to Predictive Data • More powerful analytics enable generation of Predictive (Future) data • Extend from today’s Current data to store Predictive data sets afloat Red Blue White Green HistoricalData CurrentData Predictive (Future)Data

  13. 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?

  14. Role of Forensic Data in C2 ASuW IAMD ASW

  15. Naval Problem Characterization Huge Naval Planning RTRG Naval Patterns of Life /Forensics Naval Effects NavalTarget ID/Classification NavalPredictiveAnalysis/Forecasting Increasing Level of Computational Resources Volume of Data Naval SituationalAwareness Naval Readiness Increasing Level of Data Science Design Difficulty Small Few Many Entity Types/Entity Complexity

  16. 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?

  17. 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, . . .)

  18. 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: • 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

  19. Data Science Methodology Operational Use Cases What are the operational use cases that you want to address? Operational SME 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? Data Scientist Analytic Development What analytics algorithms are required to extract the information needed to provide the analytic capabilities? Data Sourcesand 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? Computer Scientist

  20. Scoping Use Cases for Experimentation SLICE Need to define Use Case Slices through the Naval Big Data Problem Space to develop standards, patterns and practices

  21. Example of an Operational Scenario Data Analytics performed across Warfare Areas • Data-driven decision guides shaped by commander’s intent, historical decisions/results, and COA/ECOA input across all available data • 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 • Autonomous predictive SA across warfare domains • Automated consolidation of UxV capabilities/ status, to align/de-conflict tasking • Automated data security tagging at ingest ….

  22. Operational Imperative - Shortening the Kill Chain MDA MDT Find Fix Track Target Engage Assess ‘ Watch’ Chain Intel Surveil Trk/Char/ID Recon Assess Engage Assess Seamless, Transparent, Integrated, Data Centric, Agile ‘Kill’ Chain (1) Reduce Uncertainty Time Sensitive Targeting (TST) 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 simultaneously across all A2AD missions • Seamless, fully integrated Fires across the Joint Force 22

  23. 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

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