1. Integrating Geographical Information Systems and Grid Applications
Marlon Pierce
Contributions: Ahmet Sayar, Galip Aydin, Mehmet Aktas, Harshawardhan Gadgil
Community Grids Lab
Indiana University

2. Acknowledgements The real work was done by (in alphabetical order).
Mehmet Aktas
Galip Aydin
Harshawardhan Gadgil
Ahmet Sayar
Project web site:
This work was supported by NASA AIST as part of “SERVOGrid: Complexity Computational Environment”

3. Geographical Information Systems and Grid Applications
Pattern Informatics
Earthquake forecasting code developed by Prof. John Rundle (UC Davis) and collaborators.
Uses seismic archives.
Regularized Dynamic Annealing Hidden Markov Method (RDAHMM)
Time series analysis code by Dr. Robert Granat (JPL).
Can be applied to GPS and seismic archives.
Can be applied to real-time data.
Interdependent Energy Infrastructure Simulation System (IEISS)
GeoFEST
Finite element method code developed by Dr. Jay Parker (JPL) and Prof. Greg Lyzenga (JPL/Harvey Mudd College)
Uses fault models as input.
Virtual California
Prof. Rundle’s UC-Davis group
Used for forecasting
Uses fault and fault friction input

4. GIS Data Grid Work at CGL
We decided that the Data Grid components of SERVO is best implemented using standard GIS services.
Use Open Geospatial Consortium standards
Provide downloadable GIS software to the community as a side effect of SERVO research.
We implemented two cornerstone standards as Web Services (WS-I+ approach)
Web Feature Service (WFS): data service for storing abstract map features
Supports queries
Faults, GPS, seismic records
Web Map Service (WMS): generate interactive maps from WFS’s and other WMS’s.
Can be used to set up problems by extracting features (faults, seismic events, etc) from user GUIs to drive problems such as the PI code and (in near future) GeoFEST, VC.
We also built a GIS compatible UDDI and WS-Context
Browse capabilities files.
We are currently working on these steps
Improving WFS performance
Integrating WMS with video streaming technologies.
Implementing Sensor Web Enablement for streaming, real-time data.

5. Automating Pattern Informatics

6. Pattern Informatics (PI)
PI is a technique developed at University of California, Davis for analyzing earthquake seismic records to forecast regions with high future seismic activity.
They have correctly forecasted the locations of 15 of last 16 earthquakes with magnitude > 5.0 in California.
See Tiampo, K. F., Rundle, J. B., McGinnis, S. A., & Klein, W. Pattern dynamics and forecast methods in seismically active regions. Pure Ap. Geophys. 159, (2002).
PI is being applied other regions of the world, and John has gotten a lot of press.
Google “John Rundle UC Davis Pattern Informatics”

7. Pattern Informatics in a Grid Environment
PI in a Grid environment:
Hotspot forecasts are made using publicly available seismic records.
Southern California Earthquake Data Center
Advanced National Seismic System (ANSS) catalogs
Code location is unimportant, can be a service through remote execution
Results need to be stored, shared, modified
Grid/Web Services can provide these capabilities
Problems:
How do we provide programming interfaces (not just user interfaces) to the above catalogs?
How do we connect remote data sources directly to the PI code.
How do we automate this for the entire planet?
Solutions:
Use GIS services to provide the input data, plot the output data
Web Feature Service for data archives
Web Map Service for generating maps
Use HPSearch tool to tie together and manage the distributed data sources and code.

9. … Web Map Client WSDL Aggregating WMS Stubs Stubs HTTP SOAP WSDL WSDL
“REST”
WFS +
Seismic Rec.
WFS +
State Bounds …
WMS +
OnEarth

10. GIS Behind the Scenes
The web features are served up by a Web Feature Service.
Web Map Service aggregates maps
NASA OnEarth + our own renderings.
We re-implement Open Geospatial Consortium standards using Web Service Standards.
SOAP messages, WSDL service definitions.
Will allow us to separate messages from HTTP transport layer in future.
More WMS Info:
More WFS Info:
More general info, software, demos:

11. Tying It All Together: HPSearch
HPSearch is an engine for orchestrating distributed Web Service interactions
It uses an event system and supports both file transfers and data streams.
Legacy name
HPSearch flows can be scripted with JavaScript
HPSearch engine binds the flow to a particular set of remote services and executes the script.
HPSearch engines are Web Services, can be distributed interoperate for load balancing.
Boss/Worker model
ProxyWebService: a wrapper class that adds notification and streaming support to a Web Service.
More info:

12. WMS Data Filter HPSearch PI Code Runner HPSearch WFS GML WS Context
Data can be stored and retrieved from the 3rd part repository (Context Service)
WS Context
(Tambora)
WFS
(Gridfarm001)
NaradaBroker network:
Used by HPSearch engines as well as for data transfer
WMS
HPSearch
(TRex)
Data Filter
(Danube)
Virtual Data flow
WMS submits script execution request (URI of script, parameters)
HPSearch hosts an AXIS service for remote deployment of scripts
PI Code Runner
(Danube)
Accumulate Data
Run PI Code
Create Graph
Convert RAW -> GML
HPSearch
(Danube)
GML
(Danube)
Actual Data flow
HPSearch controls the Web services
Final Output pulled by the WMS
HPSearch Engines communicate using NB Messaging infrastructure

13. IEISS GUI FOR OVERLAYING OUTAGE AREA ON A MAP

14. IEISS Summary
IEISS simulates power outages resulting from damage to electrical and natural gas grids.
GIS Grid integration is similar to earlier PI application.
Primary differences:
Better support for dynamic GIS service discovery.
Better integration of distributed state monitoring (WS-Context).
Google map clients as well as modified PI clients.

15. 1-2-3 - WMS Client -> WMS Server -> UDDI -> WFS
6 - User invokes IEISS through WMS Client interface for the obtained geospatial features, and WMS Client starts a workflow session in the Context Service.
4-5 - WFS publishes the results as GML FeatureCollection document into a topic (“/NISAC/WFS”) in a pub/sub based messaging system. WFS -> WMS Server (creates a map overlay) and IEISS receive this GML document. WMS Server -> WMS Client (displays it)
WMS Client -> WMS Server -> UDDI -> WFS
WFS and WMS publish their WSDL URL to the UDDI Registry

16. 7 - On receiving invocation message, IEISS updates the shared state data to be “IEISS_IS_IN_PROGRES”. IEISS runs and produces an ESRI Shape file and then invokes shp2gml tool to convert produced Shape file to GML format. After the conversion IEISS updates shared session state to be “IEISS_COMPLETED”. As the state changes, the Context Service notifies all interested workflow entities such as WMS Client.

17. WFS-L publishes the IEISS output as a GML FeatureCollection document to NB topic ‘NISAC/WFS-L’. WMS Server is subscribed to this topic and receives the GML file then converts it to map overlay, and the Client displays the new model on the map.
8 – On receiving the notification, WMS Client makes a request to the WFS-L for the IEISS output

18. (Next set shows non-slideshow version)

20. IEISS Step by Step (Note Fig starts as 0)
WFS and WMS publish their WSDL URL to the UDDI Registry.
User starts the WMS Client on a web browser; the WMS Client displays the available features. User submits a request to the WMS Server by selecting desired features and an area on the map.
WMS Server dynamically discovers available WFSs that provide requested features through UDDI Registry and obtains their physical locations (WSDL address).
WMS Server forwards user’s request to the WFS.
WFS decodes the request, queries the database for the features and receives the response.
WFS creates a GML FeatureCollection document from the database response and publishes this document to NaradaBrokering topic ‘/NISAC/WFS’; WMS Server and IEISS receive this GML document. WMS Server creates a map overlay from the received GML document and sends it to WMS Client which in turn displays it to the user. After receiving the GML document IEISS NB Subscriber invokes gml2model tool; this tool converts GML to XML Model format to be processed by IEISS

21. IEISS Steps Continued
User invokes IEISS through WMS Client interface for the obtained geospatial features, and WMS Client starts a workflow session in the Context Service. On receiving invocation message, IEISS updates the shared state data for the workflow session to be “IEISS_IS_IN_PROGRES” on the Context Service. Both IEISS and WMS Client communicate with Context Service via asynchronous function calls by utilizing Context Respond Handler Service. IEISS runs and produces an ESRI Shape file that has the outage areas for the given region.
IEISS invokes shp2gml tool to convert produced Shape file to GML format [Fig.3]. After the conversion IEISS updates shared session state to be “IEISS_COMPLETED”. As the state changes, the Context Service notifies all interested workflow entities such as WMS Client. To notify WMS-Client, the Context Service publishes the updates to a NB topic (/NISAC/Context://IEISS/SessionStatus) from which the WMS-Client receives notifications.
WMS makes a request to the WFS-L for the IEISS output.
WFS-L publishes the IEISS output as a GML FeatureCollection document to NB topic ‘NISAC/WFS-L’. WMS Server is subscribed to this topic and receives the GML file then converts it to map overlay,
WMS Client displays the new model on the map.

22. Electric Power and Natural Gas data
Zoom-in
Zoom-out
FeatureInfo mode
Measure distance mode
Clear Distance
Drag and Drop mode
Refresh to initial map

23. Overlaid Outage Area - I
Basic Steps:
Select Energy Power AND Natural Gas Data and Update Layer List rendered on the map
Click on “Overlay Outage” button
See the outage area on the map

24. Overlaid Outage Area - II
Basic Steps:
Select Energy Power Data and Update Layer List rendered on the map
Click on “Overlay Outage” button
Use zoom-in mapping tool below to get same outage area in more detail
See the outage area on the map

25. Overlaid Outage Area - III
Basic Steps:
Select Energy Power and Natural Gas Data and Update Layer List rendered on the map
Select St. Petersburg from the “Area of Interest” dropdown list.
Click on “Overlay Outage” button.
See the outage area on the map

26. Getting Info about specific EP Data by clicking on the map
Basic Steps:
Select Energy Power Data and Update Layer List rendered on the map
Select (i) from the mapping tools below.
Click on any feature data on the map.
See the information for selected feature in pop-up window

27. Google Hybrid Map and Feature Information call to WMS
Natural Gas Layer
Electric Power Layer

28. Support for Real Time Applications

29. RDAHMM: GPS Time Series Segmentation Slide Courtesy of Robert Granat, JPL
GPS displacement (3D) length two years. Divided automatically
by HMM into 7 classes.
Features:
Dip due to aquifer drainage (days )
Hector Mine earthquake (day 626)
Noisy period at end of time series
Complex data with subtle signals is difficult for humans to analyze, leading to gaps in analysis
HMM segmentation provides an automatic way to focus attention on the most interesting parts of the time series

30. Towards Real-Time RDAHMM
A real-time version of RDHAMM could potentially be used to detect state change events in live data from a GPS station.
SCIGN maintains 125+ GPS stations, so trivially parallel RDAHHM clones can monitor state changes in the entire network.
HPSearch can help
But first we must get the data to RDAHMM.

31. NaradaBrokering: Message Transport for Distributed Services
NB is a distributed messaging software system.
NB system virtualizes transport links between components.
Supports TCP/IP, parallel TCP/IP, UDP, SSL.
See e.g. for trans-Atlantic parallel tcp/ip timings.

32. SOPAC GPS Services

33. GIS and Collaboration Tools

34. GIS and Collaboration
The previous slide illustrates an initial interface for capturing, annotating, and storing/replaying video streams.
Still images can be captured and annotated on shared white board.
Annotations are stored along with rest of system.

35. Challenges for Geographical Information System Grids
Must address performance issues.
Related workshop at GGF 15.
HTTP is not an adequate transport mechanism for moving data around.
XML representations, compression, etc.
Well established techniques from real-time collaboration can be applied to sensors
Stream archiving and playback, session management, software multicasting.
Applies to both data streams (GPS) and maps (streaming video).