Integrating Geographical Information Systems and Grid ApplicationsMarlon Pierce

Contributions: Ahmet Sayar, Galip Aydin, Mehmet Aktas, Harshawardhan GadgilCommunity Grids LabIndiana University

Acknowledgements The real work was done by (in alphabetical

order). Mehmet Aktas Galip Aydin Harshawardhan Gadgil Ahmet Sayar

Project web site: www.crisisgrid.org

This work was supported by NASA AIST as part of “SERVOGrid: Complexity Computational Environment”

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

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.

Automating Pattern Informatics

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, 2429-2467 (2002).

http://citebase.eprints.org/cgi-bin/fulltext?format=application/pdf&identifier=oai%3AarXiv.org%3Acond-mat%2F0102032

PI is being applied other regions of the world, and John has gotten a lot of press.

Google “John Rundle UC Davis Pattern Informatics”

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.

WFS+

Seismic Rec.

WSDL

WFS+

State Bounds

WSDL

WMS+

OnEarth

“REST”

…

AggregatingWMS

Stubs

Web MapClient

Stubs

WSDL

SOAPHTTP

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: http://grids.ucs.indiana.edu/ptliupages/publications/acm-gis-sayar.pdf. http://grids.ucs.indiana.edu/ptliupages/publications/Geoinformatics05_asayar.pd

f.

More WFS Info: http://grids.ucs.indiana.edu/ptliupages/publications/gwpap243.pdf

More general info, software, demos: http://www.crisisgrid.org

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: http://www.hpsearch.org

Data Filter(Danube)

PI Code Runner(Danube) Accumulate Data Run PI Code Create Graph Convert RAW -> GML

WFS(Gridfarm001)

WMS

HPSearch(TRex)

HPSearch(Danube)

HPSearch hosts an AXIS service for remote deployment of scripts

GML(Danube)

WS Context(Tambora)

NaradaBroker network: Used by HPSearch engines as well as for data transfer

Actual Data flow

HPSearch controls the Web services

Final Output pulled by the WMS

HPSearch Engines communicate using NB Messaging infrastructure

Virtual Data flow

Data can be stored and retrieved from the 3rd part repository (Context Service)

WMS submits script execution request (URI of script, parameters)

IEISS GUI FOR OVERLAYING OUTAGE AREA ON A MAP

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.

WFS and WMS publish their WSDL URL to the UDDI Registry1-2-3 - WMS Client -> WMS Server -> UDDI -> WFS4-5 - 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)

6 - User invokes IEISS through WMS Client interface for the obtained geospatial features, and WMS Client starts a workflow session in the Context Service.

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.

8 – On receiving the notification, WMS Client makes a request to the WFS-L for the IEISS output

9-10 - 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.

(Next set shows non-slideshow version)

IEISS Step by Step (Note Fig starts as 0)1. WFS and WMS publish their WSDL URL to the UDDI Registry.

2. 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.

3. WMS Server dynamically discovers available WFSs that provide requested features through UDDI Registry and obtains their physical locations (WSDL address).

4. WMS Server forwards user’s request to the WFS.5. WFS decodes the request, queries the database for the features and

receives the response.6. 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

IEISS Steps Continued7. 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.

8. 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.

9. WMS makes a request to the WFS-L for the IEISS output.10. 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,

11. WMS Client displays the new model on the map.

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

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

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

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

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

Google Hybrid Map and Feature Information call to WMS

Natural Gas Layer

Electric Power Layer

Support for Real Time Applications

RDAHMM: GPS Time Series SegmentationSlide Courtesy of Robert Granat, JPL

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

GPS displacement (3D) length two years.

Divided automaticallyby HMM into 7 classes.

Features:• Dip due to aquifer

drainage (days 120-250)

• Hector Mine earthquake (day 626)

• Noisy period at end of time series

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.

NaradaBrokering: Message Transport for Distributed Services NB is a distributed messaging

software system. http://

www.naradabrokering.org NB system virtualizes transport

links between components. Supports TCP/IP, parallel

TCP/IP, UDP, SSL. See e.g.

http://grids.ucs.indiana.edu/ptliupages/publications/AllHands2005NB-Paper.pdf for trans-Atlantic parallel tcp/ip timings.

SOPAC GPS Services

GIS and Collaboration Tools

e-Annotation Player

Archived stream player

Annotation/WB player

Archieved stream list

Real time stream list

e-Annotation Whiteboard

Real time stream player

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.

Streaming Servers

e-AnnotationPortal Server

Storage Servers

Collaborative Communication

Master/Coach

Student

Student

Student

NaradaBrokering

broker

broker

broker

broker

broker

TV

Capture Device

GLOBALMMCS

Archived Real Time (Live) StreamFrom TV and Capture Devices

Archived Streams

Stream Annotation Snapshots

broker

Collaborative e-Annotation Player

Collaborative e-Annotation Whiteboard

Instant Messenger

Real Time (Live) Stream Player

Collaborative Communication

Collaborative and Synchronous Annotation & Discussion

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).