DATA PREPAREDNESS FOR DISASTERS AND EVENTS GEOSS Future
- Slides: 12
DATA PREPAREDNESS FOR DISASTERS AND EVENTS GEOSS Future Products Workshop – Sensor Web Session Keiser Information Technology and Systems Center University of Alabama in Huntsville
Preparedness vs. Reaction • “. . . the aftermath of a major disaster is no time to be exchanging business cards. ” • Planning and preparedness can greatly improve the quality and latency of responses to events. • Good planning leads to organized and effective emergency response. • Emergency preparedness means taking action to be ready for emergencies before they happen. • The objective of emergency preparedness is to simplify decision-making during emergencies. Emergency Preparedness and Response, Some Issues and Challenges Associated with Major Emergency Incidents, Statement of William O. Jenkins, Jr. , Director Homeland Security and Justice Issues, United States Government Accountability Office Report GAO-06 -467 T, 2006.
NASA Applied Science Project • Use Event-Driven Data Delivery (ED 3) to prepare for data needs during disaster (and other) events. • Demonstrate the feasibility of an ED 3 framework to support improved data preparedness for Decision Support Systems, applications, and users. • Provide reusable framework components that can support different events, disciplines, and data and processing needs. Acknowledgements: This research is supported by the National Aeronautic and Space Administration grant NNX 12 AP 73 G. The project team includes PI Sara Graves and Co-Is Udaysankar Nair and Ken Keiser, all at the University of Alabama in Huntsville. Frank Lindsay is the NASA Applied Science program manager for this project.
Data Preparedness Plans & Services Events Plan Database Service Layer Preparedness Plan: • For this event type • Meeting this criteria, • Do this processing Decision Systems and Users Data Workflows
Decision Systems & Users Plans Service Layer Preparedness Plan: • For this event type • Meeting this criteria, • Do this processing Any authorized system may generate and submit plans Existing decision support systems Custom Applications Decision Support Systems & Users
Events Trigger Plans Event Generators Event Listener Common Alerting Protocol (CAP) Event Generators Plans Service Layer Trigger Matching Preparedness Plans • If the plan is for this type of event, and • if specifies this criteria, • then execute plan
Process Plan Workflows Request Open Jobs of Supported Processing Workflow Managers Manager Pre-negotiate access and agreements Data Repositories ISERV Data Access Plans Service Layer Receive Jobs To Be Processed Sensor Tasking Near – Future? Product Generation Process and Package Workflow managers can be specialized for different types of data and processing. Multiple workflow managers can service a single plan. Virtual Products
System/User Notifications Event Listener Plans Service Layer Event Detection Workflow Processing Workflow Managers Plan Generation Notifications by email, call-back functions, and others as necessary.
Example Flood Use Case Soil Moisture DSS Creates Preparedness Plan based on occurrence of Flood Potential Regional flood model determines potential for flood in area of interest Event / Prediction Topography Flood Potential notification issued and picked up by Event Listener Data Inputs Rainfall Matching Plan is selected by the proper Workflow Manager for execution Requested data is retrieved and higher resolution modeling run initiated Notification of plan execution and results are sent to DSS Model
ED 3 Framework Event Handling Event Listener Results Workflow Managers Data Access Event Notice Sensor Tasking Plans CAP Service Layer Event Generators Workflows Plans Decision Support Systems & Users Product Generation Process and Package
Project Participants • Project Team • Sara Graves (PI) – UAH/ITSC • Udaysankar Nair (Co-I) UAH/Atmospheric Science Dept. • Ken Keiser (Co-I) – UAH/ITSC • Jared Harper – UAH/ITSC • Michael Mc. Eniry – UAH/ITSC • Current Collaborators • Geologic Survey of Alabama (Sandy Ebersole and others) • SERVIR (Dan Irwin and others) • ISERV (Burgess Howell) Acknowledgements: This research is supported by the National Aeronautic and Space Administration grant NNX 12 AP 73 G. Frank Lindsay is the NASA Applied Science program manager for this project.
Contact Info • Ken Keiser – keiserk@uah. edu • ED 3 Reference Implementation (in development): • http: //ed 3 test. itsc. uah. edu/ed 3/subscriptions/new. php • ITSC • http: //www. itsc. uah. edu
- تفاوت future perfect و future continuous
- Future perfect simple continuous
- Data breach preparedness
- Mutually exclusive events vs not mutually exclusive events
- Chapter 36 emergency preparedness and protective practices
- Chapter 36 emergency preparedness and protective practices
- Principles of disaster management
- Emergency care first aid and disasters
- Seligman dog experiment
- Shelby county office of preparedness
- Biological preparedness
- Conditioning learning
- Conclusion of disaster preparedness