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PEARC17: Live Integrated Visualization Environment: An Experiment in Generalized Structured Frameworks for Visualization and Analysis

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PEARC17: Live Integrated Visualization Environment: An Experiment in Generalized Structured Frameworks for Visualization and Analysis

  1. 1. Live Integrated Visualization Environment: An Experiment in Generalized Structured Frameworks for Visualization and Analysis James H. Money, Ph.D. Idaho National Laboratory PEARC 17
  2. 2. Contents • Background – Problem Environment – Past Approaches/Goals • Live Integrate Visualization Environment – Data Feeds/Input Connectors – Output Connectors – Technical & System Details – Initial Application – Driver Details • Output Connector Process – Modes of Use – Dynamic Model Processing • Shortcomings • SIEVAS • Accomplishments • Examples
  3. 3. Background • Joint Intelligence Laboratory (JIL) – built in 2006 as a rapid test bed for end-to-end intelligence solutions for Department of Defense (DoD) • Initially based on the Joint Intelligence Operations Center Experimental (JIOC-X) developed early 2000s • Built to test, experiment and train using real operational data • Supported a number of advanced visualization systems including: – Knowledge Wall – Tabletop Touch Displays – Stereo Wall – Knowledge Advanced Visualization Environment (KAVE) – A CAVE by another name • Joined the JIL in late 2007 • During 2008 installed a new CAVE that is 18’x10’x10’ – largest in DoD at the time
  4. 4. Problem Environment • DoD has invested in over 18 CAVEs now, mostly used for modeling and simulation and intelligence work • Software products used by these groups included: – Presagis Vega Prime – Mechdyne vGeo – Mechdyne Conduit – GeoTime – Delta3D – Google Earth Professional – And several more… • Strategy included having the DoD pay for modifications to CAVE-enable the proprietary tools (high supports costs!)
  5. 5. Problem Environment • Various groups were attempting to process and analyze data in real time with these and other production systems • Approaches varied but all contained some major flaws in execution • The desire was to ingest real time feeds into all the environments seamlessly • Data feeds included Distributed Common Ground System (DCGS), Global Command and Control System (GCCS), as well as other lessor know systems • Showed first prototype of real-time/in-situ visualization with GOTS systems using DCGS-A and a Force Directed Layout in 2008 – this led to the development of a framework for more general purpose use
  6. 6. Past Approaches • Vendor Specific Extensions – Works out of the box – Breaks down on larger datasets/Addition cost • Direct Vendor Modifications – Costly to install – Frequently, do not meet 100% of user requirements • Custom Coding/Toolkits – Costly to build and maintain – Custom build for each CAVE • OpenGL Interceptors – Usage of desktop applications – Requires desktop to use, features do not work in immersive environment
  7. 7. Goals 1. Multi-application and domain area aware 2. Data/Model Abstraction using standard techniques 3. Simultaneous access to same data streams 4. Real-time access with DVR capability 5. Merging of simulated and live data streams 6. Utilization of off-the-shelf products
  8. 8. Live Integrated Visualization Environment • Live Integrated Visualization Environment (LIVE) is the end-to-end solution for handling the live feeds while allowing a myriad of software and tools to visualize the results • Supported geospatial data as well as non-geospatial data at the design phase • Allowed for advanced analytics in the system with verification in the CAVE (Now called “big data”) • Allowed for live viewing, recording, playback and manipulation of data • Permits remote viewing to phones, tablets and other types of displays
  9. 9. LIVE • Built on idea of “connectors” • Utilized input connectors for importing and storing data • Utilized output connectors for visualization of results • All the components were loosely coupled and connected by a data/message bus • Everything was distributed out of the box • This allows products such as Google Earth to have local data sources without changes
  10. 10. LIVE GeoTime vGeo Google Earth Vega Prime
  11. 11. LIVE Data Feeds • DCGS-A (ESRI Map Server) • GCCS Tactical Management Server (TMS) • Link 16 • Distributed Interactive Simulation (DIS)/High Level Architecture (HLA) • Cursor-on-Target (CoT) • System Toolkit (STK)
  12. 12. Live Output Connectors • Vega Prime Modules (display, control, and loading/saving) • Google Earth KML feed • Force Directed Layout (FDL) • GeoJSON for GeoTime
  13. 13. Technical Details • Built initial on Microsoft .NET 2.0 -> Later migrate to 3.5 • Data storage used Microsoft SQL Server • MessageBus custom developed using .NET Remoting • Combination of reflection, C#, and managed and unmanaged C++ to connect components • Contained system information on sessions, data sources, drivers, and configuration options • Session – List of Data Sources -> Associated Driver • DVRService will load these sessions • Also possible to use in distributed mode without centralized drivers • Output connectors required to choose session at startup or pass by configuration option
  14. 14. System Details • First developed demo application using System Toolkit for UAV applications to aid in planning tasks • Developed Google Earth connector • DVR Controls developed as desktop application • Later DVR moved into Vega Prime as billboard controls • Message bus used a publish-subscribe paradigm for messaging • Message bus sent both control (for example DVR play, stop, goto) and messages
  15. 15. Initial Application LIVE Message Bus/Web Service CAVE KML Connector Google Earth UAV Tool VLC Client Desktop Configura) on Tool Vega Prime
  16. 16. Driver Details • Components include – IDriver – IRecordable – IPlayable – ILIVEPlayable – live stream sources only – ITransformable
  17. 17. Driver Details Send Details SendObjects IDriver LIVE Message Bus Storage IRecordable IPlayable ILIVEPlayable ITransformable Store Retrive Densify DVR Details Instantiate Driver
  18. 18. Output Connector Process • Process – Session would load drivers for each data source at startup – Connect to web service for information about session – Connect to message bus – Subscribe to messages of interest – in this case DVR controls and Platform type messages – Handling dynamic loading of models in threads – Show model and changes after model load and thereafter • Google Earth (using Stand-alone middleware) – Connect and listen for message – Keep log of messages – Generate KML when requested from Google Earth – KMZ file would request periodic refresh of KML data
  19. 19. Modes of Use • Two primary modes of use for input: Drivers (Input Connectors) and Standalone applications – Drivers allowed automated processing but not real user interaction at run time – Standalone – allowed user to change items on the fly; used by the UAV Tool • Output similarly two ways to obtain data – Plugin using native SDK (Vega Prime) – Standalone application that acted as middleware between the message bus and the data (Google Earth) • This allowed swapping of simulated feeds in place of real-time data connections when network connectivity was limited. (For example, static vs. live full motion video (FMV) feeds) • Chaining with multiple instances for data analytics
  20. 20. Modes of Use
  21. 21. Dynamic Model Processing LIVE Web Service Request for Model Information Model Information Returned Request for Model Model ZIP Returned Model extracted and dynamically added Vega Prime
  22. 22. Shortcomings • Single point of failure for message bus “server” • No partitioning • No multiple session support • No user authentication support • Not multi-platform • Hard to integrate some SDKs with C# • Direct connections to databases for certain tasks • Needs to be open source
  23. 23. Scientific & Intelligence Exascale Visualization Analysis System – aka LIVE 2.0 • Fixed the above shortcomings by integrating users, multiple sessions support, and distributed “servers” • Enabled using Java primarily with ActiveMQ • Supports a range of clients from Java, C#, C++, Python, R, etc. – just need a Http Client, ActiveMQ Client, and JSON Mapper to connect • Acts much like a microservice architecture but with web services driving longer term activities • Server side uses component such as Spring Framework, Hibernate, Jackson JSON Mapper • Clients use Apache HttpClient, Jackson, and ActiveMQ client • Web services are RESTful with JSON data exchange • Native web interface for administration
  24. 24. SIEVAS • Integrated: – Unity 3D – Aspen Data – Imagery (position + orientation from EXIF data) – CT Data – N-Body particle physics – Initial Dashboard • Release on Github in ~30 days on INL’s page https://github.com/idaholab
  25. 25. Accomplishments • Added in-situ/real-time capability to immersive environments • Permitted code re-use and interoperability among display systems using disparate datasets • Decreased time to completion from months and years to days and weeks. • Utilized in production use cases for mission planning, rehearsal, and after action reviews across a range of domains • Permitted discovery of new insights and analyses for ISR based missions not before seen using traditional methods
  26. 26. Development Status Completed In-Progress (FY2017) Multiple Sessions Users, Groups, Authentication Non-Java Clients (Unity) DVR (Java & Unity) Configurable data sources Multiple data source integration Auto-Partitioning (Driver level) One-time session keys HPC Connection/C++ Client Larger volume datasets Dynamic Model Loading Dynamic isosurfaces
  27. 27. Water Security Testbed
  28. 28. UAV Application
  29. 29. Conclusion • Any questions or comments?

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