CIS2071-SPITERI STAINES-GIS 15-11-2010

8/7/2019 CIS2071-SPITERI STAINES-GIS 15-11-2010

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Integrating Geographical

Information Systems and Grid

Applications

Marlon Pierce

Contributions: Ahmet Sayar, Galip Aydin, Mehmet

Aktas, Harshawardhan GadgilCommunity Grids Lab

Indiana University


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Acknowledgements

The real work was done by (in alphabeticalorder). Mehmet Aktas

Galip Aydin

Harshawardhan Gadgil

Ahmet Sayar

Project web site:

www.crisisgrid.org

This work was supported by NASA AIST as partof SERVOGrid: Complexity ComputationalEnvironment


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Geographical Information Systems and

Grid Applications Pattern Informatics

Earthquake forecasting code developed by Prof. John Rundle (UC Davis) andcollaborators.

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. Rundles UC-Davis group

Used for forecasting

Uses fault and fault friction input


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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 SERVOresearch.

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 WFSs and otherWMSs.

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 SensorWeb Enablement for streaming, real-time data.


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Automating Pattern

Informatics


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Pattern Informatics (PI)

PI is a technique developed at University of California, Davis foranalyzing earthquake seismic records to forecast regions withhigh future seismic activity.

They have correctly forecasted the locations of 15 of last 16earthquakes 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 activeregions. 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 hasgotten a lot of press.

Google John Rundle UC Davis Pattern Informatics


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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 andcode.


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WFS+

Seismic Rec.

WSDL

WFS+

State Bounds

WSDL

WMS+

OnEarth

REST

AggregatingWMS

Stubs

Web MapClient

Stubs

WSDL

SOAPHTTP


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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 WebService 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.pdf.

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

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


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Tying It All Together: HPSearch

HPSearch is an engine for orchestrating distributed Web Serviceinteractions

It uses an event system and supports both file transfers and datastreams.

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 distributedinteroperate for load balancing.

Boss/W

orker model ProxyWebService: a wrapper class that adds notification and

streaming support to a Web Service.

More info: http://www.hpsearch.org


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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 anAXIS service for

remote deployment ofscripts

GML(Danube)

WS Context(Tambora)

NaradaBroker network:Used by HPSearch

engines as well as fordata transfer

Actual Data flow

HPSearch controls the Web services

Final Output pulled by the WMS

HPSearch Enginescommunicate using NB

Messaginginfrastructure

VirtualData

flow

Data can be stored andretrieved from the 3rd part

repository (Context Service)

WMS submits scriptexecution request

(URI of script,parameters)


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IEISS GUI FOR OVERLAYINGOUTAGE AREA ON A MAP


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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.


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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 obtainedgeospatial features, and WMS Client starts a workflow session in theContext Service.


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7 - On receiving invocation message, IEISS updates the shared state datato be IEISS_IS_IN_PROGRES . IEISS runs and produces an ESRIShape file and then invokes shp2gmltool to convert produced Shapefile to GML format. After the conversion IEISS updates shared sessionstate to be IEISS_COMPLETED. As the state changes, the ContextService notifies all interested workflow entities such as WMS Client.


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

9-10 - WFS-L publishes the IEISS output as a GML FeatureCollectiondocument to NB topic NISAC/WFS-L. WMS Server is subscribed tothis topic and receives the GML file then converts it to map overlay,

and the Client displays the new model on the map.


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(Next set shows non-

slideshow version)


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IEISS Step by Step (Note Fig starts as 0)

1. WFS and WMS publish theirWSDL URL to the UDDI Registry.

2. User starts the WMS Client on a web browser; the WMS Client displays theavailable features. User submits a request to the WMS Server by selectingdesired features and an area on the map.

3. WMS Server dynamically discovers available WFSs that provide requestedfeatures through UDDI Registry and obtains their physical locations (WSDLaddress).

4. WMS Server forwards users 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 databaseresponse 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 invokesgml2modeltool; this tool converts GML to XML Model format to beprocessed by IEISS


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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. Onreceiving invocation message, IEISS updates the shared state data for theworkflow session to be IEISS_IS_IN_PROGRES on the Context Service. BothIEISS and WMS Client communicate with Context Service via asynchronousfunction calls by utilizing Context Respond Handler Service. IEISS runs andproduces 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 allinterested workflow entities such as WMS Client. To notify WMS-Client, theContext Service publishes the updates to a NB topic(/NISAC/Context://IEISS/SessionStatus) from which the WMS-Client receivesnotifications.

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 NBtopic NISAC/WFS-L.WMS Server is subscribed to this topic and receives the GML file then converts itto map overlay,

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


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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


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Overlaid Outage Area - I

BasicSteps: SelectEnergy Power

ANDNatural Gas Data

andUpdate Layer List

renderedonthe map

Click on Overlay

Outagebutton

See the outage area on

the map


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Overlaid Outage Area - II

BasicSteps: SelectEnergy PowerData

andUpdate Layer List

renderedonthe map Click on Overlay Outage

button

Use zoom-in mappingtoolbelowto getsame outagearea in more detail

See the outage area onthemap


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Overlaid Outage Area - III

BasicSteps: SelectEnergy Powerand

Natural Gas Dataand

Update Layer Listrendered

onthe map

SelectSt. Petersburg from

the Area ofInterest

dropdown list.

Click on Overlay Outage

button.

See the outage area onthe

map


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Getting Info about specific EP Data by clickingon the map

BasicSteps: SelectEnergy PowerDataand

Update Layer Listrenderedon

the map Select(i) from the mapping

tools below.

Click onany feature data on

the map.

See the information for

selectedfeature in pop-up

window


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Google Hybrid Map andFeature Information call to WMS

Natural Gas Layer

Electric Power Layer


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Support for Real Time

Applications


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

Complex data with subtle signals is difficult for humans toanalyze, leading to gaps in analysis

HMM segmentation provides an automatic way to focus attentionon the most interesting parts of the time series

GPS displacement (3D)

length two years.

Divided automatically

by 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


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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 triviallyparallel RDAHHM clones can monitor state changes

in the entire network.

HPSearch can help

But first we must get the data to RDAHMM.


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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, parallelTCP/IP, UDP, SSL.

See e.g.http://grids.ucs.indiana.edu/ptliupages/publications/AllHands2005NB-Paper.pdffor trans-Atlantic parallel tcp/ip timings.


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SOPAC GPS Services


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GIS and Collaboration Tools


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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.


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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).

Documents

CIS2071-SPITERI STAINES-GIS 15-11-2010