CIS2071-SPITERI STAINES-GIS 15-11-2010
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Transcript of 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).