A Framework for Geospatial Web Services for Public Health June 8, 2009 URISA Public Health Conference  Leslie Lenert, MD, ...
Our Latest Enemy (Ally?)
National Trends in ILI Data from BioSense
Percent ILI Reported to BioSense by Federal Region, 10/04/2008 to 05/30/2009
But really, don’t we all want this?
Our Goal Should Tracking and Forecasting Outbreak Progression to Improve Control GLEaMviz.org
State of Public Health Surveillance:  An Information Supply Chain <ul><li>Incentives for sharing are not strong </li></ul>...
Future Model: Information Ecology <ul><li>Leverage  Existing Capacity </li></ul><ul><ul><li>Distribute resources and infra...
What is Service Oriented Architecture? <ul><li>“ A paradigm for organizing and utilizing distributed capabilities that may...
Implications of SOA – Massimo Pezzini
Implications of SOA – Massimo Pezzini
Implications of SOA – Massimo Pezzini
Business Process as Services <ul><li>Process definition is essential for service creation </li></ul><ul><li>Integrated Bus...
Implications of SOA – Dan Ellis
Why is SOA Important? <ul><li>SOA is a fundamental shift in information systems development, comparable to the shift from ...
Implications of SOA <ul><li>Technical challenges </li></ul><ul><ul><li>Application/services integration middleware technol...
An Example SOA Governance
Source:  http://www.fgdc.gov/training/nsdi-training-program/materials/IntroGeoBusinessPlanning.ppt#328,7,Geospatial Profil...
GRID Concept <ul><li>A computing infrastructure… </li></ul><ul><ul><li>Expands circle of trusted resources outside of the ...
Types of Grids <ul><li>Computational Grids </li></ul><ul><ul><li>Focused on the application of several computers to a sing...
Computational Grids <ul><li>Most famous/infamous </li></ul>A massively distributed computing environment composed of over ...
@Home Model Extended Grid application models protein folding & misfolding (1224  teraflops , as of 23 Sept 2007) Grid appl...
Collaboration Grid Example <ul><li>Presentation, visualization and interactive environments </li></ul><ul><li>Runs on the ...
Data Grid Examples caBIG – Cancer Research Datagrid GEON – Geosciences Network Datagrid EGEE / CERN - The world's largest ...
Commercial Grid Products Tier 1 Tier 2
Open Source Grid Software/Projects
Commercial Grid “Consumers” Amazon Elastic Compute Cloud & Amazon Simple Storage Service
Future Model: Information Ecology <ul><li>Leverage  Existing Capacity </li></ul><ul><ul><li>Distribute resources and infra...
Grid as a supporting technical framework  for public health… <ul><ul><li>Lets experts retain stewardship of information </...
 
Examining PHGrid and Security:  Leveraging the Expertise of Others… Grid Authentication and Authorization with Reliably Di...
BioSurveillance POC:  Federated Search <ul><li>Primary requirements </li></ul><ul><ul><li>Demonstrate the capability to sh...
RODSA-DAI <ul><li>Foundation: Real Time Outbreak Detection System and Globus Grid Toolkit </li></ul><ul><ul><li>RODS - ~20...
Poison Control Data Access & Integration  <ul><li>Demonstrate access and visualization of poison control call data via web...
Poison Control Data Access & Integration
Linking GIS and non GIS sources: GIPSE (Geocoded  Interoperable Population Summary Exchange) Format <ul><li>Obtain consens...
GIPSE is a set of matrixes  Date range Zip codes 1,2,3,5,0,6,… 2,1,4,7,0,3,… … . … . … . counts Influenza Gastrointestinal...
GIPSE+ adds cross tabs for age category and gender Age category Date range Zip codes Sex Date range Zip codes + Influenza ...
GIPSE SERVICES REGISTRY
Clinical or Surveillance Database GIPSE Data set cache Periodic Extract of GIPSE data sets Result #1 GIPSE Delivery Servic...
PHGrid / NHIN Interoperability Research <ul><li>Goal: </li></ul><ul><ul><li>Leverage investments in quality assessment for...
How does GIS fit into this? <ul><li>By providing  </li></ul><ul><li>Visualization services </li></ul><ul><li>Spatial analy...
Geospatial Services <ul><li>GIS community has been using web services for about 10 years through image and feature service...
PH-DGInet <ul><li>To help the geospatial community leverage each other investments CDC has been exploring the use of the D...
PH-DGINet: Enabling shareable GIS services <ul><li>Distributed/shared web services framework based on ESRI GIS technology ...
ArcIMS (10 years of this)   DATABASE DATABASE MAP SERVER LAN ArcIMS Consumers
PH-DGInet WAN (Multiple PH-DGInet nodes) LAN PH-DGInet SERVER CDC  PH-DGInet Node LAN PH-DGInet Consumers (Internet Explor...
Aggregation Query:  State accessing Veterans Admin Data at CDC PH-DGINet South Carolina  Consumers DGInet SERVER South Car...
Aggregation Query: State access it’s own hospital data and VA data at CDC PH-DGINet South Carolina  Consumers DGInet SERVE...
PH-DGInet Architecture <ul><li>All open source software except ArcGIS Server </li></ul><ul><li>Only need a browser to disc...
PH-DGINet: Increased Interoperability July 23, 2009 Draft Clients DGINet Services  Data Service Providers OGC Map Viewers ...
3-D Visualization through a browser
States currently exploring PH-DGInet
PH-DGInet Screens
PH-DGInet Toolbar
PH-DGInet Geospatial Data Nodes
PH-DGInet Geoprocessing Sevices
Adding in an open source (OGC) web service
Create and Maintain Bookmarks
Toolbox – NPDS Query National Poison Control Data Service Basemaps are coming from Redlands, California and the Poison Con...
Tool – Aggregation Query <ul><li>Select data from multiple nodes that will build one choropleth map and one histrogram fro...
Summary <ul><li>SOA  and Grid will help the public health community share services to support application development and ...
Questions?
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A Framework for Geospatial Web Services for Public Health by Dr. Leslie Lenert

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A Framework for Geospatial Web Services for Public Health
by Leslie Lenert, MD, MS, FACMI, Director
National Center for Public Health Informatics, CCHIS, CDC

June 8 2009 URISA Public Health Conference

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URISA Membership Committee Chair
http://www.gisinpublichealth.org

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  • 07/23/09 Draft
  • Speakers Notes: Public Health Surveillance has traditionally followed a one way data model – from practitioners to state and local health and on ward to CDC. Past biosurveillance models have even circumvented that model, with data often bypassing the state and local health department. Regardless of the steps, the one way flow has led to a system that is resource intensive to both the data providers and the CDC. Moreover, the current model has many non-technical hurdles that must be addressed including: Politics of control of data has been the primary obstacle to formation of a national system Much existing data remains siloed at the Local/ State level – accessibility and visualization limited Building systems non collaboratively leads to low adoption rates
  • Having recognized the limitations of the previous model, the Public Health community has started to explore the feasibility of a federated data architecture, where the work of public health surveillance and practice is distributed amongst the national public health community. In this model, operations like biosurveillance will: Leverage the existing investments of the state and local public health communities. These may include the expertise of the scientific community, existing data sets and standards, as well as the inclusion of industry and academic partners that can facilitate biosurveillance practice. These will be supported by distributed information technology frameworks, under the general heading of the public health grid. The goal is to create a shared services platform that will allow the public health community to leverage the investment of it’s partners and in the end serve the public more effectively. On framework that is being explored to support this model is the Public Health Distributed Geospatial Intelligence Framework.
  • -Amazon’s Elastic Computing Cloud (EC2) provides a service interface to grid computing capability ($0.10/instance hour) -Amazon’s Simple Storage Service (S3) provides a service interface to remote storage ($0.15/GB/month storage) -EBay’s Trading Web Services provides service interface to listing and managing auctions
  • COE – cross-enterprise body with multi-disciplinary representation from Centers, Institutes and Offices. Responsibilities: Service portfolio plan Development Priorities Reusability Funding Ownership Pros Consolidated decision making Optimal use of resources Cons Can slow adoption of new standards Requires discipline Which services to develop? Which are the clearest components of our IT infrastructure that can be reused the most by our applications? Which services to do first? Which services deliver the highest return? Is a potential service actually new and reusable? Or should we reuse or modify existing services? Who&apos;s going to pay for the development and maintenance of this service? Who owns the service? Does ownership change throughout development, operation and maintenance Registry/Repository Service Descriptions Service Metadata Security Metadata Service Management System Performance Metrics Usage Metrics Executive Committee Enforces decision making Decides on funding Architecture Committee Evaluate technology
  • So, to summarize, there are three main types of grids: computational, collaborative and data, or a dynamic combination of the three
  • There are many misconceptions about grid computing. The most popular by far is the association with grid computing with the search for extraterrestrial intelligence, or SETI. SETI does utilize grid software to accomplish its task and has led to a unique public involvement in science where anybody with a PC at home may sign up to allow their computer to process massive amounts of radio signal data received by satellite.
  • The SETI model has been hugely popular and has branched off into other science domains such as Folding@home, Africa@home and FightAids@home. Thousands of compute cycles at home across the planet are being offered by individuals to solve these huge computational problems at a fraction of the cost. Serious organizations are behind these efforts—National Science Foundation, National Institutes of Health, Google, Dell, Apple and Intel, to name a few
  • Grid computing also enables people-to-people and organization-to-organization communication through collaboration grids. These grids combine resources used to support group-to-group interactions, large-scale distributed meetings, collaborative work sessions, seminars, lectures, tutorials, and training.
  • Besides enables large-scale computational and collaboration networks, grid computing also enables access to data and databases distributed throughout the world. caBIG is an active research grid developed by the National Cancel Institute to interconnect cancer research centers. CERN is building the largest particle physics laboratory ever. It went online this past May. The European Community is spending billions of euros building the grid infrastructure necessary to support the data produced by the LHC.
  • To give you a sense of the commercial activity relative to grid computing, the following companies all have grid products on offer today.
  • Much is also happening in the open source world of grid computing. This is a small sample of the open source grid projects currently in operation globally. PRAGMA is pacific rim focused. EGEE is an operational grid currently handling over 100,000 transactions per day, and growing. The Globus Alliance, for example, is based at Argonne National Laboratory , the University of Southern California&apos;s Information Sciences Institute , the University of Chicago , the University of Edinburgh , the Swedish Center for Parallel Computers , and the National Center for Supercomputing Applications (NCSA) . The Alliance produces open-source software that is central to science and engineering activities totalling nearly a half-billion dollars internationally and is the substrate for significant Grid products offered by leading IT companies.
  • What is most interesting, to me, at least, are the major companies that are currently using grid computing to support their infrastructure. Second Life is grid-based. Google is grid-based. Goodyear, Boeing, AMD, Adobe, the department of energy and Partners Healthcare all use grid computing in their product development lifecycle. And Amazon.com offers companies the ability to run their services on their grid infrastructure on a per CPU per hour basis.
  • Having recognized the limitations of the previous model, the Public Health community has started to explore the feasibility of a federated data architecture, where the work of public health surveillance and practice is distributed amongst the national public health community. In this model, operations like biosurveillance will: Leverage the existing investments of the state and local public health communities. These may include the expertise of the scientific community, existing data sets and standards, as well as the inclusion of industry and academic partners that can facilitate biosurveillance practice. These will be supported by distributed information technology frameworks, under the general heading of the public health grid. The goal is to create a shared services platform that will allow the public health community to leverage the investment of it’s partners and in the end serve the public more effectively. On framework that is being explored to support this model is the Public Health Distributed Geospatial Intelligence Framework.
  • Public health official at state or regional or national levels configures subscription services (defines GIPSE sets to be computed) using GIPSE Subscription Service. GIPSE Subscription Service reports the creation of a new service to the GIPSE registry, along with meta data GIPSE Subscription Service sends specifications for data retrieval to the GIPSE Publication Service GIPSE Publication service periodically computes specified GIPSE objects and sends them to a data cache GIPSE objects cache stores the objects for retrieval by the Pop Delivery Service A user using the PH GRID data visualization tool wants to query summary data for a geo-region The visualization tool uses the grid query service to determine the appropriate regional sources of GIPSE data from the GIPSE registry. The grid query tool then uses Population Summary Delivery Services to retrieve the relevant GIPSE Pop Summary Delivery Service retrieves appropriate object or creates a new object by combining existing objects (for example, it might combine 30 one day GIPSE objects into a 30-day object. The population summary delivery service returns the GIPSE from a data source in response to the request from a Grid reporting service The Grid Query Service combines reports from several different GIPSE services to produce an integrated GIPSE report The visualization program receives the Integrated GIPSE report. The visualization program uses other services to perform statistical testing. The visualization program displays the integrated GIPSE report using geographical display services.
  • 07/23/09 Draft Public Health DGINet is a pilot program that NCPHI has been exploring to test the federated model. PH-DGINet builds on an Eight year – DOD certified program known as DGInet. This program is supported by a distributed data and service model that has ~30 nodes upon which several federal DoD and intelligence agencies share information and spatial imagery. In 2007 and 2008, NCPHI started exploring the viability of the DGInet, and its service oriented architecture, to understand if and how it could support the needs of the public health community, and in particular, biosurviellance. Services Oriented Architecture (SOA) GIS enterprise solution for geospatial data services and geoprocessing services Data Management Services : Provides services for auto-data loading/management of multi-terabyte databases Web Map Services : Allows for easy discovery, fusion and display of geospatial and geospatial intelligence data from multiple remote organizations via low bandwidth web services Web Geoprocessing Services : Allows for easy discovery/utilization of server side GIS based analytical services from multiple remote organizations via web client
  • This examples illustrates how an end user in South Carolina would access information for South Carolina but on the CDC node. In addition it shows how the PH-DGINet Distributed Aggregation Query allows the end user to build a query based on information from the CDC node. When the user selects the Distributed Aggregation Query function, a dialog appears requesting the user to select the syndrome, geography, node, age group, gender, and date range. This process generates a SQL statement that is passed to the identified node. The node processes the SQL statement, identifies the row level data that meets specifications of the SQL statement, aggregates the data by the identified geography, and then sends it back to the end user. Once the information is received by the client side application, a chloropleth map and histogram are created and displayed. In this example the South Carolina end user sees aggregate counts of flu by county in the VA medical centers in South Carolina from the CDC node.
  • This examples illustrates how an end user in South Carolina would access information for South Carolina on the South Carolina node and information from the CDC node. In addition it shows how the PH-DGINet Distributed Aggregation Query allows the end user to build a query based on information from both the South Carolina and the CDC node. Similar to the previous example, the end user will select the Distributed Aggregation Query function, a dialog appears requesting the user to select the syndrome, geography, node, age group, gender, and date range but in this example the end user will select two nodes to query, the South Carolina and CDC node. This process generates a SQL statement that is passed to both nodes this time. The nodes processes the SQL statement, identifies the row level data that meets specifications of the SQL statement, aggregates the data by the identified geography, and then sends it back to the end user. Once the information is received by the client side application, the information is aggregated from both nodes by the identified geography, a chloropleth map and histogram are created and displayed. This time there is a histogram that is created with aggregate information from each node and the height represents the total count from both nodes. In this example, the South Carolina end user sees aggregate counts of flu by county in the VA medical centers from the CDC node and hospital data from the South Carolina node combined.
  • 07/23/09 Draft
  • A Framework for Geospatial Web Services for Public Health by Dr. Leslie Lenert

    1. 1. A Framework for Geospatial Web Services for Public Health June 8, 2009 URISA Public Health Conference Leslie Lenert, MD, MS, FACMI, Director National Center for Public Health Informatics, CCHIS, CDC
    2. 2. Our Latest Enemy (Ally?)
    3. 3. National Trends in ILI Data from BioSense
    4. 4. Percent ILI Reported to BioSense by Federal Region, 10/04/2008 to 05/30/2009
    5. 5. But really, don’t we all want this?
    6. 6. Our Goal Should Tracking and Forecasting Outbreak Progression to Improve Control GLEaMviz.org
    7. 7. State of Public Health Surveillance: An Information Supply Chain <ul><li>Incentives for sharing are not strong </li></ul><ul><li>Incentives for holding on to data are strong </li></ul><ul><li>People providing data do not have access to all the information. </li></ul>CDC State, County, and Local Health Departments Laboratories Practitioners
    8. 8. Future Model: Information Ecology <ul><li>Leverage Existing Capacity </li></ul><ul><ul><li>Distribute resources and infrastructure </li></ul></ul><ul><ul><li>Increase flexibility and scalability </li></ul></ul><ul><li>Provide Local Control of data and services </li></ul><ul><ul><li>Reduces political barriers </li></ul></ul><ul><ul><li>Address many privacy concerns </li></ul></ul><ul><li>Foster Collaboration to define requirements, priorities, develop, and deploy technology </li></ul>Users / Experts Standards, Services, Guidance Local/ State Data Local/State Health Dept Surveillance & Informatics Capacity Analysis / Visualization Capacity National Data Academic / Industry Partners Local/ State Data CDC & Other Federal Agencies Scientific and Public Health Priorities Public Health Grid
    9. 9. What is Service Oriented Architecture? <ul><li>“ A paradigm for organizing and utilizing distributed capabilities that may be under the control of different ownership domains.” – OASIS Reference Model for Service Oriented Architecture 1.0, CS 1, 19 July 2006 1. </li></ul><ul><li>Loosely coupled, modular service components </li></ul><ul><ul><li>Not a new concept (e.g. decades of mainframe services, and programmed service components) </li></ul></ul><ul><li>Business process centric (not data centric) </li></ul><ul><li>Standards based interoperability </li></ul><ul><li>Delegation of functional responsibilities across multiple systems </li></ul><ul><li>Modular development of applications, as collections of service oriented business components </li></ul><ul><li>Collaborative, multidisciplinary, distributed </li></ul><ul><li>SOA Principles </li></ul><ul><ul><li>Modularity, componentization, reuse, granularity, interoperability </li></ul></ul><ul><ul><li>Compliance with standards (both common and industry-specific) </li></ul></ul><ul><ul><li>Service provisioning (description, semantics, visibility, service interface, contract & policy, execution context), consumption, choreography, auditing and governance </li></ul></ul><ul><ul><li>1. OASIS is the Organization for the Advancement of Structured Information Standards </li></ul></ul>
    10. 10. Implications of SOA – Massimo Pezzini
    11. 11. Implications of SOA – Massimo Pezzini
    12. 12. Implications of SOA – Massimo Pezzini
    13. 13. Business Process as Services <ul><li>Process definition is essential for service creation </li></ul><ul><li>Integrated Business & Technology View </li></ul><ul><li>Aligned to Lines of Business </li></ul><ul><li>Not necessarily “web services” </li></ul><ul><li>Delivers return through agility, not just reuse </li></ul><ul><li>Technology becomes the “easy” part of SOA </li></ul>Theo Beack - http://theobeack.typepad.com/technology/2006/02/soa_approaches_.html
    14. 14. Implications of SOA – Dan Ellis
    15. 15. Why is SOA Important? <ul><li>SOA is a fundamental shift in information systems development, comparable to the shift from mainframe to client-server computing. – Gartner </li></ul><ul><li>Agility, adaptability </li></ul><ul><li>Ready data sharing across diverse partners and technical platforms </li></ul><ul><li>Increased manageability and scalability </li></ul><ul><li>Rapid application development to address new demands </li></ul><ul><ul><li>Application assembly; composition of shared services via portals </li></ul></ul><ul><ul><li>Distributed workload, lower cost </li></ul></ul><ul><ul><li>Synergy of distributed services development (e.g. WWW) </li></ul></ul><ul><li>Major industry trend </li></ul><ul><ul><li>IT middleware platform evolution </li></ul></ul><ul><ul><li>Powerful application architectures (e.g. Google, eBay, Amazon,…) </li></ul></ul>
    16. 16. Implications of SOA <ul><li>Technical challenges </li></ul><ul><ul><li>Application/services integration middleware technologies, atop the application server (e.g. ESB, Service Directory & Repository, Orchestration, Automated message routing/brokering, Transformation, etc.) </li></ul></ul><ul><ul><li>Escalating technical complexity and cost with use of what Gartner calls “SOA Backplane” technologies </li></ul></ul><ul><ul><li>Management complexity of service orchestration and Event-Driven Architectures </li></ul></ul><ul><li>Layered service architecture topologies </li></ul><ul><ul><li>Solutions (presentation & dialog) </li></ul></ul><ul><ul><li>Process Services (orchestration layer) </li></ul></ul><ul><ul><li>Core Business Services Layer </li></ul></ul><ul><ul><li>Underlying Services </li></ul></ul><ul><ul><li>Utility Services </li></ul></ul><ul><li>Paradigm shift in systems design and development practices </li></ul><ul><ul><li>Multi-disciplinary collaborations among varied disciplines and skills (EA, KM, Information Architecture, Business Intelligence, BPM) </li></ul></ul><ul><ul><li>Designing to distribution on a global grid (network) of shared services, across diverse partners and unanticipated consumers </li></ul></ul><ul><ul><li>Supporting reliable services assembly, orchestration and execution in unanticipated patterns (e.g mashups) </li></ul></ul>
    17. 17. An Example SOA Governance
    18. 18. Source: http://www.fgdc.gov/training/nsdi-training-program/materials/IntroGeoBusinessPlanning.ppt#328,7,Geospatial Profile of the FEA Geospatial Line of Business and Federal Enterprise Architecture FEDERAL OMB pressure for Consolidation of GIS Resources and Shared Services
    19. 19. GRID Concept <ul><li>A computing infrastructure… </li></ul><ul><ul><li>Expands circle of trusted resources outside of the organization </li></ul></ul><ul><ul><li>Provides dependable, consistent, pervasive and inexpensive access to data and applications. </li></ul></ul><ul><ul><li>By pooling assets into a virtual system, a grid allows data owners to share data and applications across boundaries while maintaining control. </li></ul></ul>
    20. 20. Types of Grids <ul><li>Computational Grids </li></ul><ul><ul><li>Focused on the application of several computers to a single problem at the same time – usually to a scientific or technical problem that requires a great number of computer processing cycles or access to large amounts of data. </li></ul></ul><ul><ul><li>(virtual super-computer) </li></ul></ul><ul><li>Collaboration / Access Grids </li></ul><ul><ul><li>Focused on the collection of resources and technologies that enables large format audio and video based collaboration between groups of people in different locations. </li></ul></ul><ul><li>Data Grids </li></ul><ul><ul><li>Focused on he controlled sharing and management of large amounts of distributed data. </li></ul></ul><ul><li>Dynamic combinations or all on same platform </li></ul>
    21. 21. Computational Grids <ul><li>Most famous/infamous </li></ul>A massively distributed computing environment composed of over 3 million Internet-connected computers launched in May 1999—has led to a unique public involvement in science. Three million PCs deliver 6,000 CPU years per day—the fastest (admittedly special-purpose) computer in the world
    22. 22. @Home Model Extended Grid application models protein folding & misfolding (1224 teraflops , as of 23 Sept 2007) Grid application models the way malaria spreads in Africa and the potential impact that new anti-malarial drugs may have on the region Grid application models the design of new anti-HIV drugs based on molecular structure ( in silico ) 1
    23. 23. Collaboration Grid Example <ul><li>Presentation, visualization and interactive environments </li></ul><ul><li>Runs on the same grid as the computational grid </li></ul><ul><li>These combined resources are used to support group-to-group interactions, large-scale distributed meetings, collaborative work sessions, seminars, lectures, tutorials, and training </li></ul>
    24. 24. Data Grid Examples caBIG – Cancer Research Datagrid GEON – Geosciences Network Datagrid EGEE / CERN - The world's largest particle physics laboratory... where the web was born (LHC – The Large Hadron Collider, May, 2008) DataGrid –EU funded resource of shared large-scale databases TeraGrid –Shares resources at San Diego Supercomputer Center, Indiana University, Oak Ridge National Laboratory, National Center for Supercomputing Applications, Pittsburgh Supercomputing Center, Purdue University, Texas Advanced Computing Center, University of Chicago/Argonne National Laboratory, and the National Center for Atmospheric Research
    25. 25. Commercial Grid Products Tier 1 Tier 2
    26. 26. Open Source Grid Software/Projects
    27. 27. Commercial Grid “Consumers” Amazon Elastic Compute Cloud & Amazon Simple Storage Service
    28. 28. Future Model: Information Ecology <ul><li>Leverage Existing Capacity </li></ul><ul><ul><li>Distribute resources and infrastructure </li></ul></ul><ul><ul><li>Increase flexibility and scalability </li></ul></ul><ul><li>Provide Local Control of data and services </li></ul><ul><ul><li>Reduces political barriers </li></ul></ul><ul><ul><li>Address many privacy concerns </li></ul></ul><ul><li>Foster Collaboration to define requirements, priorities, develop, and deploy technology </li></ul>Users / Experts Standards, Services, Guidance Local/ State Data Local/State Health Dept Surveillance & Informatics Capacity Analysis / Visualization Capacity National Data Academic / Industry Partners Local/ State Data CDC & Other Federal Agencies Scientific and Public Health Priorities Public Health Grid
    29. 29. Grid as a supporting technical framework for public health… <ul><ul><li>Lets experts retain stewardship of information </li></ul></ul><ul><ul><li>Flexibility allows Integration, Interoperability & Data Access between Silos </li></ul></ul><ul><ul><li>In the long-term the Cost & Time to Re-engineer Existing App Silos falls </li></ul></ul>Other Public Health Application Silos Epi-X BioSense
    30. 31. Examining PHGrid and Security: Leveraging the Expertise of Others… Grid Authentication and Authorization with Reliably Distributed Services (GAARDS) is a series of tools developed by Ohio State to enhance the open source Gobus Toolkit. Provides enterprise level administrative tools for managing users, federated identities, trust, credential delegation, group management, access control policy, and integration between grid and non-grid-based security domains.
    31. 32. BioSurveillance POC: Federated Search <ul><li>Primary requirements </li></ul><ul><ul><li>Demonstrate the capability to share and visualize biosurveillance data: </li></ul></ul><ul><ul><ul><li>Within a State </li></ul></ul></ul><ul><ul><ul><li>Between States </li></ul></ul></ul><ul><ul><ul><li>Between States and CDC </li></ul></ul></ul><ul><ul><li>Aggregate data under control of state, share results with external users </li></ul></ul><ul><ul><li>Combine and visualize results in the form of maps and simple analysis (e.g. Epi Curve) </li></ul></ul>Goal: Explore standards based federated frameworks to promote distributed data stewardship, analytical access, and collaboration between participating stakeholders. Inform NCPHI and its public health and commercial partners of best practices and potential issues to this approach, and provide a foundation to evaluate existing and emerging interoperability protocols.
    32. 33. RODSA-DAI <ul><li>Foundation: Real Time Outbreak Detection System and Globus Grid Toolkit </li></ul><ul><ul><li>RODS - ~20 production instances across US </li></ul></ul><ul><ul><li>Globus – Leading Open Source Grid Middleware; used in NCI’s caBIG, GeonGrid </li></ul></ul><ul><li>Hypothesis: Extending RODS with Globus Services allows the ability to query across installations, and visualize data from disparate / secured nodes </li></ul>
    33. 34. Poison Control Data Access & Integration <ul><li>Demonstrate access and visualization of poison control call data via web services </li></ul><ul><li>Display data over multiple days over multiple call classifications </li></ul><ul><li>Combine and visualize results in the form of maps and simple charts </li></ul>Goal: Research ability to augment public health situational awareness, by accessing non-clinical data sources of public health importance, based on secure web services
    34. 35. Poison Control Data Access & Integration
    35. 36. Linking GIS and non GIS sources: GIPSE (Geocoded Interoperable Population Summary Exchange) Format <ul><li>Obtain consensus on most relevant elements </li></ul><ul><li>Create common biosurveillance data structure aligning to AHIC / HITSP standards </li></ul><ul><li>Develop interfaces to existing partner biosurveillance systems </li></ul><ul><li>Distribute & refine using open source principles </li></ul><ul><li>Proposed elements </li></ul><ul><ul><li>Geo region </li></ul></ul><ul><ul><ul><li>Zip, zip3, etc. </li></ul></ul></ul><ul><ul><li>Condition </li></ul></ul><ul><ul><li>Condition classifier </li></ul></ul><ul><ul><li>Count </li></ul></ul><ul><ul><li>Date range </li></ul></ul><ul><ul><li>Cross tabs for age category, sex </li></ul></ul>Goal: Facilitate multi-state public health situational awareness with simple, common data interchange service based on a subset of key biosurveillance data elements
    36. 37. GIPSE is a set of matrixes Date range Zip codes 1,2,3,5,0,6,… 2,1,4,7,0,3,… … . … . … . counts Influenza Gastrointestinal Rash
    37. 38. GIPSE+ adds cross tabs for age category and gender Age category Date range Zip codes Sex Date range Zip codes + Influenza cases
    38. 39. GIPSE SERVICES REGISTRY
    39. 40. Clinical or Surveillance Database GIPSE Data set cache Periodic Extract of GIPSE data sets Result #1 GIPSE Delivery Service (Grid node) GIPSE Publication Service Result #2 Result #3 Firewall GIPSE Registry Quicksilver Viewer
    40. 41. PHGrid / NHIN Interoperability Research <ul><li>Goal: </li></ul><ul><ul><li>Leverage investments in quality assessment for public health </li></ul></ul><ul><ul><li>Demonstrate that the NHIN and PHGrid are complimentary and compatible platforms for distributed services and federated data </li></ul></ul><ul><ul><li>Demonstrate that the NHIN and PHGrid are capable of trusting data from each other </li></ul></ul><ul><li>Challenges </li></ul><ul><ul><li>with bidirectional communication between the two environments: </li></ul></ul><ul><ul><li>Differences in the level of security of data that resides in each environment </li></ul></ul><ul><ul><li>Differences in the core functions of each platform </li></ul></ul>
    41. 42. How does GIS fit into this? <ul><li>By providing </li></ul><ul><li>Visualization services </li></ul><ul><li>Spatial analytic services </li></ul><ul><li>Geospatial data services </li></ul><ul><li>Modeling services </li></ul><ul><li>Task services (routing, geocoding,etc) </li></ul><ul><li>Integration of geocoded and location coded data </li></ul>
    42. 43. Geospatial Services <ul><li>GIS community has been using web services for about 10 years through image and feature services using products such as ArcIMS. </li></ul><ul><li>GIS Portals such as Geography Network, Geospatial One Stop, and a number of other geospatial portals have provided one stop shopping for data services similar to web service registries. </li></ul><ul><li>There are currently thousands a geospatial services across the global GIS community but many of these are not being leveraged as well as they should. </li></ul><ul><li>The public health community has lagged behind other communities such as defense, natural resources, and transportation in using and sharing these services to support program initiatives. </li></ul><ul><li>Because the public health community has lagged behind communities they have not been able to leverage the financial and intellectual investments of their partners there re-creating similar efforts across programs at all levels of public health. </li></ul>
    43. 44. PH-DGInet <ul><li>To help the geospatial community leverage each other investments CDC has been exploring the use of the Distributed Geospatial Intelligence Network (DGInet) but modifying it for public health </li></ul><ul><li>DGInet is a GRID consisting of geospatial services in the defense/intel community. </li></ul><ul><li>DGInet consists of: </li></ul><ul><ul><li>Services Oriented Architecture (SOA)/ open standards based portal environment </li></ul></ul><ul><ul><li>Designed for non-GIS savvy military planners and intelligence analysts </li></ul></ul><ul><ul><li>Scaleable/N-Tier/Backward Compatible/C2B-B2B Architecture </li></ul></ul><ul><ul><li>Designed to the lowest common denominator </li></ul></ul><ul><ul><li>Server side operations </li></ul></ul><ul><ul><li>30 nodes across the globe </li></ul></ul><ul><li>Public Health DGInet </li></ul><ul><li>Currently has 3 nodes (CDC, ESRI, and the South Carolina Department of Health and Environment) </li></ul><ul><li>Can create a distributed aggregated choropleth map and histrogram from across nodes in real time. </li></ul><ul><li>Can be used to discover geospatial services across the network to use in application development or desktop analysis </li></ul>
    44. 45. PH-DGINet: Enabling shareable GIS services <ul><li>Distributed/shared web services framework based on ESRI GIS technology </li></ul><ul><li>PH-DGINet allows Access to data, services and tools available within a single portal per organization </li></ul><ul><ul><li>PH-DGInet portal can be customizable to organization, role, individual </li></ul></ul><ul><ul><li>DGInet supports discretionary service and data sharing </li></ul></ul><ul><ul><li>PH-DGINet can be extended with simple analytical tools </li></ul></ul><ul><li>Exploring service integration / interoperability with Globus stack </li></ul>
    45. 46. ArcIMS (10 years of this) DATABASE DATABASE MAP SERVER LAN ArcIMS Consumers
    46. 47. PH-DGInet WAN (Multiple PH-DGInet nodes) LAN PH-DGInet SERVER CDC PH-DGInet Node LAN PH-DGInet Consumers (Internet Explorer) (PH-DGInet Explorer) (ArcGIS Client) LAN PH-DGInet SERVER EPA PH-DGInet Node LAN LAN PH-DGInet SERVER CA PH-DGInet Node LAN LAN PH-DGInet SERVER NIH PH-DGInet Node LAN
    47. 48. Aggregation Query: State accessing Veterans Admin Data at CDC PH-DGINet South Carolina Consumers DGInet SERVER South Carolina PH-DGINet Node Event Database DGInet SERVER CDC PH-DGINet Node Event Database DGInet SERVER Other State PH-DGINet Node Event Database Access Row Level Data Aggregate Row Level Data (Web Client) SQL Query Aggregated VA Data
    48. 49. Aggregation Query: State access it’s own hospital data and VA data at CDC PH-DGINet South Carolina Consumers DGInet SERVER South Carolina PH-DGINet Node Event Database DGInet SERVER CDC PH-DGINet Node Event Database DGInet SERVER Other State PH-DGINet Node Event Database Access Row Level Data Aggregate Row Level Data Access Row Level Data Aggregate Row Level Data (Web Client) SQL Query Aggregated VA Data
    49. 50. PH-DGInet Architecture <ul><li>All open source software except ArcGIS Server </li></ul><ul><li>Only need a browser to discover geospatial data, map it, and do geospatial analytics </li></ul><ul><li>At the same time, GIS Analyst can leverage the same services in their desktops for additional analysis and developers can leverage the same services to build applications. </li></ul>
    50. 51. PH-DGINet: Increased Interoperability July 23, 2009 Draft Clients DGINet Services Data Service Providers OGC Map Viewers DGINet Map Viewer ArcGIS Desktop ArcGIS Explorer Commercial Map Viewers Google Earth ArcGIS Server WMS Server GML Server WFS Server KML Server ArcIMS Server DGINet Web Services - GetMetadataService -LocatorService -GetListOfProductNamesService -GetProductService -GetFeaturesService -GetMapImage Service -GetAnnotationService -GetExtractService -DGINetSystemServiceBroadcastService -OGCProvidersManagerService -SystemMonitorService -DGINetSystemServiceUDDIService -DGINetSystemServiceScheduleService -DGINetSystemServiceNotifyService -LOS_GeoprocessingService DGINet Tools - Bookmark -Download/zip -Annotation -Query Builder Data Management Service DGINet Content Manager API Custom HTML Web Pages DGINet -CMS
    51. 52. 3-D Visualization through a browser
    52. 53. States currently exploring PH-DGInet
    53. 54. PH-DGInet Screens
    54. 55. PH-DGInet Toolbar
    55. 56. PH-DGInet Geospatial Data Nodes
    56. 57. PH-DGInet Geoprocessing Sevices
    57. 58. Adding in an open source (OGC) web service
    58. 59. Create and Maintain Bookmarks
    59. 60. Toolbox – NPDS Query National Poison Control Data Service Basemaps are coming from Redlands, California and the Poison Control data is coming from Denver. The poison control data is only a data service, not a feature service so PH-DGInet is building the spatial component on the fly based on the geography listed in the data.
    60. 61. Tool – Aggregation Query <ul><li>Select data from multiple nodes that will build one choropleth map and one histrogram from data from multiple nodes, in real time. </li></ul><ul><li>Background map layer comes from Redlands, California, hospital data comes from South Carolina, and the VA data comes from CDC </li></ul><ul><li>So three nodes are used to make this one map </li></ul>
    61. 62. Summary <ul><li>SOA and Grid will help the public health community share services to support application development and analysis but at the same time reduce cost and share investments </li></ul><ul><ul><li>Help create an information ecology </li></ul></ul><ul><li>The data stay at the source so the currency of the data is up to the data provider, not a clearinghouse </li></ul><ul><ul><li>Quality and collaboration </li></ul></ul><ul><li>Grid technologies enhance the access to geospatial SOA </li></ul><ul><ul><li>service registry in PH-DGInet will provide a common place to access and consume these services </li></ul></ul><ul><li>Geospatial SOA and Grid computing </li></ul><ul><ul><li>will allow both non-savvy GIS users to consume and view the data through the PH-DGInet viewer but at the same time, the </li></ul></ul><ul><ul><li>GIS analysts can consume and use the same services in ArcGIS desktop for analysis </li></ul></ul>
    62. 63. Questions?

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